{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "30e02d05",
   "metadata": {},
   "source": [
    "# Pipeline Regression With GASearchCV\n",
    "\n",
    "This notebook shows how to tune a scikit-learn `Pipeline` with `GASearchCV`. The objective is still pipeline prediction, but the example now includes a stronger regression workflow, holdout metrics, optimizer telemetry, and advanced optimizer controls.\n",
    "\n",
    "## Menu\n",
    "\n",
    "1. [Problem Setup](#problem-setup)\n",
    "2. [Baseline Pipeline](#baseline-pipeline)\n",
    "3. [Define Pipeline Search Space](#define-pipeline-search-space)\n",
    "4. [Configure GASearchCV](#configure-gasearchcv)\n",
    "5. [Evaluate Predictions](#evaluate-predictions)\n",
    "6. [Inspect Search Cost and Telemetry](#inspect-search-cost-and-telemetry)\n",
    "7. [Visualize the Search](#visualize-the-search)\n",
    "8. [Practical Notes](#practical-notes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a386a0f",
   "metadata": {},
   "source": [
    "## Problem Setup\n",
    "\n",
    "We use the diabetes regression dataset and tune a pipeline containing `StandardScaler` and `GradientBoostingRegressor`. Pipeline parameters use the usual sklearn double-underscore syntax, such as `regressor__max_depth`."
   ]
  },
  {
   "cell_type": "code",
   "id": "7f70149b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-20T05:33:07.993489Z",
     "iopub.status.busy": "2026-06-20T05:33:07.993166Z",
     "iopub.status.idle": "2026-06-20T05:33:16.885104Z",
     "shell.execute_reply": "2026-06-20T05:33:16.883974Z"
    },
    "ExecuteTime": {
     "end_time": "2026-06-20T18:49:10.046919400Z",
     "start_time": "2026-06-20T18:49:05.977562800Z"
    }
   },
   "source": [
    "import warnings\n",
    "from pprint import pprint\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "from sklearn.model_selection import KFold, train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn_genetic import (\n",
    "    EvolutionConfig,\n",
    "    GASearchCV,\n",
    "    OptimizationConfig,\n",
    "    PopulationConfig,\n",
    "    RuntimeConfig,\n",
    ")\n",
    "from sklearn_genetic.callbacks import ConsecutiveStopping, DeltaThreshold, TimerStopping\n",
    "from sklearn_genetic.plots import plot_fitness_evolution, plot_search_space\n",
    "from sklearn_genetic.schedules import ExponentialAdapter, InverseAdapter\n",
    "from sklearn_genetic.space import Categorical, Continuous, Integer\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "\n",
    "RANDOM_STATE = 42"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "id": "b3d20984",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-20T05:33:16.888970Z",
     "iopub.status.busy": "2026-06-20T05:33:16.888392Z",
     "iopub.status.idle": "2026-06-20T05:33:16.908152Z",
     "shell.execute_reply": "2026-06-20T05:33:16.906672Z"
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     "end_time": "2026-06-20T18:49:10.119093400Z",
     "start_time": "2026-06-20T18:49:10.054919200Z"
    }
   },
   "source": [
    "data = load_diabetes(as_frame=True)\n",
    "X = data.data\n",
    "y = data.target\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,\n",
    "    y,\n",
    "    test_size=0.30,\n",
    "    random_state=RANDOM_STATE,\n",
    ")\n",
    "\n",
    "cv = KFold(n_splits=4, shuffle=True, random_state=RANDOM_STATE)\n",
    "\n",
    "print(f\"Training shape: {X_train.shape}\")\n",
    "print(f\"Test shape: {X_test.shape}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training shape: (309, 10)\n",
      "Test shape: (133, 10)\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "id": "dd6f96e6",
   "metadata": {},
   "source": [
    "## Baseline Pipeline\n",
    "\n",
    "A baseline gives us a sanity check before optimizing. The helper below returns common regression metrics where lower RMSE/MAE is better and higher R2 is better."
   ]
  },
  {
   "cell_type": "code",
   "id": "d43371d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-20T05:33:16.911911Z",
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     "start_time": "2026-06-20T18:49:10.120093600Z"
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   },
   "source": [
    "def make_pipeline(**regressor_kwargs):\n",
    "    return Pipeline(\n",
    "        [\n",
    "            (\"scaler\", StandardScaler()),\n",
    "            (\n",
    "                \"regressor\",\n",
    "                GradientBoostingRegressor(random_state=RANDOM_STATE, **regressor_kwargs),\n",
    "            ),\n",
    "        ]\n",
    "    )\n",
    "\n",
    "\n",
    "def regression_metrics(estimator, X_eval, y_eval):\n",
    "    predictions = estimator.predict(X_eval)\n",
    "    rmse = mean_squared_error(y_eval, predictions) ** 0.5\n",
    "    return {\n",
    "        \"r2\": r2_score(y_eval, predictions),\n",
    "        \"rmse\": rmse,\n",
    "        \"mae\": mean_absolute_error(y_eval, predictions),\n",
    "    }\n",
    "\n",
    "\n",
    "baseline = make_pipeline()\n",
    "baseline.fit(X_train, y_train)\n",
    "baseline_metrics = regression_metrics(baseline, X_test, y_test)\n",
    "baseline_metrics"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'r2': 0.43031868253825245,\n",
       " 'rmse': 55.45552342062193,\n",
       " 'mae': 44.71796061792019}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "id": "1acaba01",
   "metadata": {},
   "source": [
    "## Define Pipeline Search Space\n",
    "\n",
    "`GASearchCV` receives the same parameter names you would use with sklearn grid search. The values are `sklearn-genetic-opt` space objects instead of fixed grids or scipy distributions."
   ]
  },
  {
   "cell_type": "code",
   "id": "85a090fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-20T05:33:17.125861Z",
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     "shell.execute_reply": "2026-06-20T05:33:17.129469Z"
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    "ExecuteTime": {
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     "start_time": "2026-06-20T18:49:10.688365900Z"
    }
   },
   "source": [
    "param_grid = {\n",
    "    \"regressor__n_estimators\": Integer(40, 180),\n",
    "    \"regressor__learning_rate\": Continuous(0.01, 0.20, distribution=\"log-uniform\"),\n",
    "    \"regressor__max_depth\": Integer(1, 4),\n",
    "    \"regressor__min_samples_leaf\": Integer(1, 12),\n",
    "    \"regressor__subsample\": Continuous(0.65, 1.0),\n",
    "    \"regressor__loss\": Categorical([\"squared_error\", \"absolute_error\", \"huber\"]),\n",
    "}"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "id": "c72ee7b7",
   "metadata": {},
   "source": [
    "## Configure GASearchCV\n",
    "\n",
    "This search uses performance and quality controls: smart initialization, warm starts, adaptive schedules, diversity control, fitness sharing, local refinement, cache reuse, and automatic parallel backend selection."
   ]
  },
  {
   "cell_type": "code",
   "id": "4e7cc424",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-20T05:33:17.133834Z",
     "iopub.status.busy": "2026-06-20T05:33:17.133394Z",
     "iopub.status.idle": "2026-06-20T05:35:21.345749Z",
     "shell.execute_reply": "2026-06-20T05:35:21.344249Z"
    },
    "ExecuteTime": {
     "end_time": "2026-06-20T18:51:12.460759700Z",
     "start_time": "2026-06-20T18:49:10.697573Z"
    }
   },
   "source": [
    "search = GASearchCV(\n",
    "    estimator=make_pipeline(),\n",
    "    param_grid=param_grid,\n",
    "    scoring=\"neg_root_mean_squared_error\",\n",
    "    criteria=\"max\",\n",
    "    cv=cv,\n",
    "    evolution_config=EvolutionConfig(\n",
    "        population_size=12,\n",
    "        generations=10,\n",
    "        crossover_probability=ExponentialAdapter(initial_value=0.8, end_value=0.4, adaptive_rate=0.15),\n",
    "        mutation_probability=InverseAdapter(initial_value=0.25, end_value=0.08, adaptive_rate=0.25),\n",
    "        tournament_size=3,\n",
    "        elitism=True,\n",
    "        keep_top_k=3,\n",
    "    ),\n",
    "    population_config=PopulationConfig(\n",
    "        initializer=\"smart\",\n",
    "        warm_start_configs=[\n",
    "            {\n",
    "                \"regressor__n_estimators\": 100,\n",
    "                \"regressor__learning_rate\": 0.05,\n",
    "                \"regressor__max_depth\": 2,\n",
    "                \"regressor__min_samples_leaf\": 4,\n",
    "                \"regressor__subsample\": 0.85,\n",
    "                \"regressor__loss\": \"squared_error\",\n",
    "            }\n",
    "        ],\n",
    "    ),\n",
    "    runtime_config=RuntimeConfig(\n",
    "        n_jobs=-1,\n",
    "        parallel_backend=\"auto\",\n",
    "        use_cache=True,\n",
    "        verbose=True,\n",
    "        return_train_score=False,\n",
    "    ),\n",
    "    optimization_config=OptimizationConfig(\n",
    "        local_search=True,\n",
    "        local_search_top_k=2,\n",
    "        local_search_steps=1,\n",
    "        local_search_radius=0.20,\n",
    "        diversity_control=True,\n",
    "        diversity_threshold=0.30,\n",
    "        diversity_stagnation_generations=3,\n",
    "        diversity_mutation_boost=1.8,\n",
    "        random_immigrants_fraction=0.10,\n",
    "        fitness_sharing=True,\n",
    "        sharing_radius=0.40,\n",
    "    ),\n",
    ")\n",
    "\n",
    "callbacks = [\n",
    "    DeltaThreshold(threshold=0.01, generations=5, metric=\"fitness_best\"),\n",
    "    ConsecutiveStopping(generations=7, metric=\"fitness_best\"),\n",
    "    TimerStopping(total_seconds=120),\n",
    "]\n",
    "\n",
    "search.fit(X_train, y_train, callbacks=callbacks)\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " gen evals           avg          best     div  unique  stag     mut   sel             events\n",
      "---- ----- ------------- ------------- ------- ------- ----- ------- ----- ------------------\n",
      "   0    12     -61.14987     -59.23358   0.682   1.000     0       -     - -                 \n",
      "   1    24     -60.65867     -59.23358   0.288   0.667     1   0.200     3 dup=3,share       \n",
      "   2    24     -60.71806     -58.92132   0.364   0.750     0   0.256     3 div,imm=3,dup=10,s\n",
      "   3    24     -59.74359     -58.92132   0.303   0.667     1   0.193     3 dup=5,share       \n",
      "   4    24     -59.91321     -58.92132   0.333   0.667     2   0.177     3 dup=10,share      \n",
      "   5    24     -62.38514     -58.92132   0.409   0.667     3   0.165     3 dup=13,share      \n",
      "INFO: TimerStopping callback met its criteria\n",
      "INFO: Stopping the algorithm\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GASearchCV(crossover_probability=<sklearn_genetic.schedules.schedulers.ExponentialAdapter object at 0x000002ACC9B397F0>,\n",
       "           cv=KFold(n_splits=4, random_state=42, shuffle=True),\n",
       "           diversity_control=True, diversity_mutation_boost=1.8,\n",
       "           diversity_stagnation_generations=3, diversity_threshold=0.3,\n",
       "           estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                                     ('regressor',\n",
       "                                      Gradi...\n",
       "                                                                   'regressor__min_samples_leaf': 4,\n",
       "                                                                   'regressor__n_estimators': 100,\n",
       "                                                                   'regressor__subsample': 0.85}]),\n",
       "           population_size=12, return_train_score=True,\n",
       "           runtime_config=RuntimeConfig(n_jobs=-1,\n",
       "                                        pre_dispatch='2*n_jobs',\n",
       "                                        error_score=nan,\n",
       "                                        return_train_score=False,\n",
       "                                        use_cache=True,\n",
       "                                        parallel_backend='auto',\n",
       "                                        verbose=True),\n",
       "           scoring='neg_root_mean_squared_error', sharing_radius=0.4)"
      ],
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
       ".features .features-container table td {\n",
       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>GASearchCV(crossover_probability=&lt;sklearn_genetic.schedules.schedulers.ExponentialAdapter object at 0x000002ACC9B397F0&gt;,\n",
       "           cv=KFold(n_splits=4, random_state=42, shuffle=True),\n",
       "           diversity_control=True, diversity_mutation_boost=1.8,\n",
       "           diversity_stagnation_generations=3, diversity_threshold=0.3,\n",
       "           estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                                     (&#x27;regressor&#x27;,\n",
       "                                      Gradi...\n",
       "                                                                   &#x27;regressor__min_samples_leaf&#x27;: 4,\n",
       "                                                                   &#x27;regressor__n_estimators&#x27;: 100,\n",
       "                                                                   &#x27;regressor__subsample&#x27;: 0.85}]),\n",
       "           population_size=12, return_train_score=True,\n",
       "           runtime_config=RuntimeConfig(n_jobs=-1,\n",
       "                                        pre_dispatch=&#x27;2*n_jobs&#x27;,\n",
       "                                        error_score=nan,\n",
       "                                        return_train_score=False,\n",
       "                                        use_cache=True,\n",
       "                                        parallel_backend=&#x27;auto&#x27;,\n",
       "                                        verbose=True),\n",
       "           scoring=&#x27;neg_root_mean_squared_error&#x27;, sharing_radius=0.4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GASearchCV</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('estimator',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">estimator</td>\n",
       "            <td class=\"value\">Pipeline(step...9516060816))])</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('cv',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">cv</td>\n",
       "            <td class=\"value\">KFold(n_split... shuffle=True)</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('param_grid',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">param_grid</td>\n",
       "            <td class=\"value\">{&#x27;regressor__learning_rate&#x27;: &lt;sklearn_gene...002ACC9B39550&gt;, &#x27;regressor__loss&#x27;: &lt;sklearn_gene...002ACC9B39400&gt;, &#x27;regressor__max_depth&#x27;: &lt;sklearn_gene...002ACC9AEE350&gt;, &#x27;regressor__min_samples_leaf&#x27;: &lt;sklearn_gene...002ACC9AEE210&gt;, ...}</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('scoring',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">scoring</td>\n",
       "            <td class=\"value\">&#x27;neg_root_mean_squared_error&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('population_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">population_size</td>\n",
       "            <td class=\"value\">12</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('generations',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">generations</td>\n",
       "            <td class=\"value\">10</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('crossover_probability',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">crossover_probability</td>\n",
       "            <td class=\"value\">&lt;sklearn_gene...002ACC9B397F0&gt;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('mutation_probability',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">mutation_probability</td>\n",
       "            <td class=\"value\">&lt;sklearn_gene...002ACC9B396A0&gt;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('keep_top_k',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">keep_top_k</td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs</td>\n",
       "            <td class=\"value\">-1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('return_train_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">return_train_score</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('evolution_config',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">evolution_config</td>\n",
       "            <td class=\"value\">EvolutionConf...MuPlusLambda&#x27;)</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('population_config',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">population_config</td>\n",
       "            <td class=\"value\">PopulationCon...ared_error&#x27;}])</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('runtime_config',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">runtime_config</td>\n",
       "            <td class=\"value\">RuntimeConfig... verbose=True)</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('optimization_config',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">optimization_config</td>\n",
       "            <td class=\"value\">OptimizationC...ction_cv=None)</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('local_search',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">local_search</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('local_search_top_k',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">local_search_top_k</td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('local_search_radius',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">local_search_radius</td>\n",
       "            <td class=\"value\">0.2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('diversity_control',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">diversity_control</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('diversity_threshold',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">diversity_threshold</td>\n",
       "            <td class=\"value\">0.3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('diversity_stagnation_generations',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">diversity_stagnation_generations</td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('diversity_mutation_boost',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">diversity_mutation_boost</td>\n",
       "            <td class=\"value\">1.8</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fitness_sharing',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fitness_sharing</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sharing_radius',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">sharing_radius</td>\n",
       "            <td class=\"value\">0.4</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tournament_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tournament_size</td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('elitism',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">elitism</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('criteria',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">criteria</td>\n",
       "            <td class=\"value\">&#x27;max&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('algorithm',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">algorithm</td>\n",
       "            <td class=\"value\">&#x27;eaMuPlusLambda&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('refit',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">refit</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">pre_dispatch</td>\n",
       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('error_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">error_score</td>\n",
       "            <td class=\"value\">nan</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('log_config',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">log_config</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('use_cache',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">use_cache</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start_configs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">warm_start_configs</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('parallel_backend',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">parallel_backend</td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('population_initializer',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">population_initializer</td>\n",
       "            <td class=\"value\">&#x27;smart&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('local_search_steps',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">local_search_steps</td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_immigrants_fraction',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_immigrants_fraction</td>\n",
       "            <td class=\"value\">0.1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('adaptive_selection',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">adaptive_selection</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('selection_pressure_min',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">selection_pressure_min</td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('selection_pressure_max',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">selection_pressure_max</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('offspring_diversity_retries',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">offspring_diversity_retries</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sharing_alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">sharing_alpha</td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('final_selection',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">final_selection</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('final_selection_top_k',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">final_selection_top_k</td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('final_selection_cv',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">final_selection_cv</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">X_</a></td>\n",
       "           <td class=\"fitted-att-type\">DataFrame</td>\n",
       "           <td>          age... x 10 columns]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">best_estimator_</a></td>\n",
       "           <td class=\"fitted-att-type\">Pipeline</td>\n",
       "           <td>Pipeline(step...9516060816))])</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">best_index_</a></td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>133</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">best_params_</a></td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{&#x27;re...te&#x27;: 0.0875444183193989, &#x27;re...ss&#x27;: &#x27;sq...or&#x27;, &#x27;re...th&#x27;: 1, &#x27;re...af&#x27;: 9, ...}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">best_score_</a></td>\n",
       "           <td class=\"fitted-att-type\">float</td>\n",
       "           <td>-58.82</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">cv_results_</a></td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{&#x27;me...me&#x27;: [np.float64(0.8632683157920837), np.float64(0.8658415079116821), np.float64(0.9247797131538391), np.float64(1.5119764804840088), ...], &#x27;me...me&#x27;: [np.float64(0....7496337890625), np.float64(0....9754371643066), np.float64(0....4149227142334), np.float64(0....6505546569824), ...], &#x27;me...re&#x27;: [np.float64(-60.31850154975417), np.float64(-6...4046828555245), np.float64(-5...3582652202905), np.float64(-60.21950347099971), ...], &#x27;me...re&#x27;: [np.float64(-4...3099979723964), np.float64(-2...0023997839722), np.float64(-5...0289346405715), np.float64(-5...7671444606044), ...], ...}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">estimator_</a></td>\n",
       "           <td class=\"fitted-att-type\">Pipeline</td>\n",
       "           <td>Pipeline(step...9516060816))])</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">final_selection_results_</a></td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{&#x27;ca...es&#x27;: [], &#x27;changed&#x27;: False, &#x27;cv&#x27;: None, &#x27;enabled&#x27;: False, ...}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">fit_stats_</a></td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{&#x27;ca...ts&#x27;: 1, &#x27;cr...ls&#x27;: 133, &#x27;du...es&#x27;: 0, &#x27;ev...es&#x27;: 134, ...}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">multimetric_</a></td>\n",
       "           <td class=\"fitted-att-type\">bool</td>\n",
       "           <td>False</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">n_features_in_</a></td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>10</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">n_splits_</a></td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>4</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">refit_time_</a></td>\n",
       "           <td class=\"fitted-att-type\">float</td>\n",
       "           <td>0.0728</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">scorer_</a></td>\n",
       "           <td class=\"fitted-att-type\">_Scorer</td>\n",
       "           <td>make_scorer(r...hod=&#x27;predict&#x27;)</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">y_</a></td>\n",
       "           <td class=\"fitted-att-type\">Series[float64](309,)</td>\n",
       "           <td>225    208.0\n",
       "...dtype: float64</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: Pipeline</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___scaler__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-copy;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-copy;\">\n",
       "            copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_mean',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-with_mean;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
       "            with_mean\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-with_mean;\">\n",
       "            with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_std',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-with_std;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
       "            with_std\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-with_std;\">\n",
       "            with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-feature_names_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=feature_names_in_,-ndarray%20of%20shape%20%28n_features_in_%2C%29\">\n",
       "            feature_names_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-feature_names_in_;\">\n",
       "            feature_names_in_: ndarray of shape (`n_features_in_`,)<br><br>Names of features seen during :term:`fit`. Defined only when `X`<br>has feature names that are all strings.<br><br>.. versionadded:: 1.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[object](10,)</td>\n",
       "           <td>[&#x27;age&#x27;,&#x27;sex&#x27;,&#x27;bmi&#x27;,...,&#x27;s4&#x27;,&#x27;s5&#x27;,&#x27;s6&#x27;]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-mean_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=mean_,-ndarray%20of%20shape%20%28n_features%2C%29%20or%20None\">\n",
       "            mean_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-mean_;\">\n",
       "            mean_: ndarray of shape (n_features,) or None<br><br>The mean value for each feature in the training set.<br>Equal to ``None`` when ``with_mean=False`` and ``with_std=False``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](10,)</td>\n",
       "           <td>[0.,0.,0.,...,0.,0.,0.]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>10</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_samples_seen_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=n_samples_seen_,-int%20or%20ndarray%20of%20shape%20%28n_features%2C%29\">\n",
       "            n_samples_seen_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_samples_seen_;\">\n",
       "            n_samples_seen_: int or ndarray of shape (n_features,)<br><br>The number of samples processed by the estimator for each feature.<br>If there are no missing samples, the ``n_samples_seen`` will be an<br>integer, otherwise it will be an array of dtype int. If<br>`sample_weights` are used it will be a float (if no missing data)<br>or an array of dtype float that sums the weights seen so far.<br>Will be reset on new calls to fit, but increments across<br>``partial_fit`` calls.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">float64</td>\n",
       "           <td>309</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-scale_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=scale_,-ndarray%20of%20shape%20%28n_features%2C%29%20or%20None\">\n",
       "            scale_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-scale_;\">\n",
       "            scale_: ndarray of shape (n_features,) or None<br><br>Per feature relative scaling of the data to achieve zero mean and unit<br>variance. Generally this is calculated using `np.sqrt(var_)`. If a<br>variance is zero, we can&#x27;t achieve unit variance, and the data is left<br>as-is, giving a scaling factor of 1. `scale_` is equal to `None`<br>when `with_std=False`.<br><br>.. versionadded:: 0.17<br>   *scale_*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](10,)</td>\n",
       "           <td>[0.05,0.05,0.05,...,0.05,0.05,0.05]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-var_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=var_,-ndarray%20of%20shape%20%28n_features%2C%29%20or%20None\">\n",
       "            var_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-var_;\">\n",
       "            var_: ndarray of shape (n_features,) or None<br><br>The variance for each feature in the training set. Used to compute<br>`scale_`. Equal to ``None`` when ``with_mean=False`` and<br>``with_std=False``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](10,)</td>\n",
       "           <td>[0.,0.,0.,...,0.,0.,0.]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div>\n",
       "        <div class=\"features fitted\">\n",
       "          <details>\n",
       "            <summary>\n",
       "              <div class=\"arrow\"></div>\n",
       "              <div>10 features</div>\n",
       "              <div class=\"image-container\" title=\"Copy all output features\">\n",
       "                <i class=\"copy-paste-icon\"\n",
       "                  onclick=\"\n",
       "                  event.stopPropagation();\n",
       "                  event.preventDefault();\n",
       "                  copyFeatureNamesToClipboard(this);\n",
       "                  \"\n",
       "                >\n",
       "                </i>\n",
       "              </div>\n",
       "            </summary>\n",
       "            <div class=\"features-container\">\n",
       "                <table class=\"features-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr>\n",
       "          <td>age</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>sex</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>bmi</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>bp</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>s1</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>s2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>s3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>s4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>s5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>s6</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </div>\n",
       "          </details>\n",
       "        </div>\n",
       "    <div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___regressor__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-learning_rate;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=learning_rate,-float%2C%20default%3D0.1\">\n",
       "            learning_rate\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-learning_rate;\">\n",
       "            learning_rate: float, default=0.1<br><br>Learning rate shrinks the contribution of each tree by `learning_rate`.<br>There is a trade-off between learning_rate and n_estimators.<br>Values must be in the range `[0.0, inf)`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0875444183193989</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_estimators;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
       "            n_estimators\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_estimators;\">\n",
       "            n_estimators: int, default=100<br><br>The number of boosting stages to perform. Gradient boosting<br>is fairly robust to over-fitting so a large number usually<br>results in better performance.<br>Values must be in the range `[1, inf)`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">91</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('subsample',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-subsample;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=subsample,-float%2C%20default%3D1.0\">\n",
       "            subsample\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-subsample;\">\n",
       "            subsample: float, default=1.0<br><br>The fraction of samples to be used for fitting the individual base<br>learners. If smaller than 1.0 this results in Stochastic Gradient<br>Boosting. `subsample` interacts with the parameter `n_estimators`.<br>Choosing `subsample &lt; 1.0` leads to a reduction of variance<br>and an increase in bias.<br>Values must be in the range `(0.0, 1.0]`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.7276569516060816</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-min_samples_leaf;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
       "            min_samples_leaf\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-min_samples_leaf;\">\n",
       "            min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, values must be in the range `[1, inf)`.<br>- If float, values must be in the range `(0.0, 1.0)` and `min_samples_leaf`<br>  will be `ceil(min_samples_leaf * n_samples)`.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">9</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_depth',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_depth;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=max_depth,-int%20or%20None%2C%20default%3D3\">\n",
       "            max_depth\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_depth;\">\n",
       "            max_depth: int or None, default=3<br><br>Maximum depth of the individual regression estimators. The maximum<br>depth limits the number of nodes in the tree. Tune this parameter<br>for best performance; the best value depends on the interaction<br>of the input variables. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.<br>If int, values must be in the range `[1, inf)`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance or None, default=None<br><br>Controls the random seed given to each Tree estimator at each<br>boosting iteration.<br>In addition, it controls the random permutation of the features at<br>each split (see Notes for more details).<br>It also controls the random splitting of the training data to obtain a<br>validation set if `n_iter_no_change` is not None.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary &lt;random_state&gt;`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('loss',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-loss;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=loss,-%7B%27squared_error%27%2C%20%27absolute_error%27%2C%20%27huber%27%2C%20%27quantile%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27squared_error%27\">\n",
       "            loss\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-loss;\">\n",
       "            loss: {&#x27;squared_error&#x27;, &#x27;absolute_error&#x27;, &#x27;huber&#x27;, &#x27;quantile&#x27;},             default=&#x27;squared_error&#x27;<br><br>Loss function to be optimized. &#x27;squared_error&#x27; refers to the squared<br>error for regression. &#x27;absolute_error&#x27; refers to the absolute error of<br>regression and is a robust loss function. &#x27;huber&#x27; is a<br>combination of the two. &#x27;quantile&#x27; allows quantile regression (use<br>`alpha` to specify the quantile).<br>See<br>:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_quantile.py`<br>for an example that demonstrates quantile regression for creating<br>prediction intervals with `loss=&#x27;quantile&#x27;`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;squared_error&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('criterion',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-criterion;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=criterion,-%7B%27friedman_mse%27%2C%20%27squared_error%27%7D%2C%20default%3D%27friedman_mse%27\">\n",
       "            criterion\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-criterion;\">\n",
       "            criterion: {&#x27;friedman_mse&#x27;, &#x27;squared_error&#x27;}, default=&#x27;friedman_mse&#x27;<br><br>This parameter has no effect.<br><br>.. versionadded:: 0.18<br><br>.. deprecated:: 1.9<br>   `criterion` is deprecated and will be removed in 1.11.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_split',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-min_samples_split;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
       "            min_samples_split\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-min_samples_split;\">\n",
       "            min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, values must be in the range `[2, inf)`.<br>- If float, values must be in the range `(0.0, 1.0]` and `min_samples_split`<br>  will be `ceil(min_samples_split * n_samples)`.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-min_weight_fraction_leaf;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
       "            min_weight_fraction_leaf\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-min_weight_fraction_leaf;\">\n",
       "            min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.<br>Values must be in the range `[0.0, 0.5]`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-min_impurity_decrease;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
       "            min_impurity_decrease\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-min_impurity_decrease;\">\n",
       "            min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br>Values must be in the range `[0.0, inf)`.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('init',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-init;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=init,-estimator%20or%20%27zero%27%2C%20default%3DNone\">\n",
       "            init\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-init;\">\n",
       "            init: estimator or &#x27;zero&#x27;, default=None<br><br>An estimator object that is used to compute the initial predictions.<br>``init`` has to provide :term:`fit` and :term:`predict`. If &#x27;zero&#x27;, the<br>initial raw predictions are set to zero. By default a<br>``DummyEstimator`` is used, predicting either the average target value<br>(for loss=&#x27;squared_error&#x27;), or a quantile for the other losses.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_features',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_features;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=max_features,-%7B%27sqrt%27%2C%20%27log2%27%7D%2C%20int%20or%20float%2C%20default%3DNone\">\n",
       "            max_features\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_features;\">\n",
       "            max_features: {&#x27;sqrt&#x27;, &#x27;log2&#x27;}, int or float, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, values must be in the range `[1, inf)`.<br>- If float, values must be in the range `(0.0, 1.0]` and the features<br>  considered at each split will be `max(1, int(max_features * n_features_in_))`.<br>- If &quot;sqrt&quot;, then `max_features=sqrt(n_features)`.<br>- If &quot;log2&quot;, then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>Choosing `max_features &lt; n_features` leads to a reduction of variance<br>and an increase in bias.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-alpha;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=alpha,-float%2C%20default%3D0.9\">\n",
       "            alpha\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-alpha;\">\n",
       "            alpha: float, default=0.9<br><br>The alpha-quantile of the huber loss function and the quantile<br>loss function. Only if ``loss=&#x27;huber&#x27;`` or ``loss=&#x27;quantile&#x27;``.<br>Values must be in the range `(0.0, 1.0)`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.9</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: int, default=0<br><br>Enable verbose output. If 1 then it prints progress and performance<br>once in a while (the more trees the lower the frequency). If greater<br>than 1 then it prints progress and performance for every tree.<br>Values must be in the range `[0, inf)`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_leaf_nodes;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
       "            max_leaf_nodes\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_leaf_nodes;\">\n",
       "            max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>Values must be in the range `[2, inf)`.<br>If None, then unlimited number of leaf nodes.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-warm_start;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-warm_start;\">\n",
       "            warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just erase the<br>previous solution. See :term:`the Glossary &lt;warm_start&gt;`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('validation_fraction',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-validation_fraction;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=validation_fraction,-float%2C%20default%3D0.1\">\n",
       "            validation_fraction\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-validation_fraction;\">\n",
       "            validation_fraction: float, default=0.1<br><br>The proportion of training data to set aside as validation set for<br>early stopping. Values must be in the range `(0.0, 1.0)`.<br>Only used if ``n_iter_no_change`` is set to an integer.<br><br>.. versionadded:: 0.20</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_iter_no_change;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=n_iter_no_change,-int%2C%20default%3DNone\">\n",
       "            n_iter_no_change\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_iter_no_change;\">\n",
       "            n_iter_no_change: int, default=None<br><br>``n_iter_no_change`` is used to decide if early stopping will be used<br>to terminate training when validation score is not improving. By<br>default it is set to None to disable early stopping. If set to a<br>number, it will set aside ``validation_fraction`` size of the training<br>data as validation and terminate training when validation score is not<br>improving in all of the previous ``n_iter_no_change`` numbers of<br>iterations.<br>Values must be in the range `[1, inf)`.<br>See<br>:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_early_stopping.py`.<br><br>.. versionadded:: 0.20</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=1e-4<br><br>Tolerance for the early stopping. When the loss is not improving<br>by at least tol for ``n_iter_no_change`` iterations (if set to a<br>number), the training stops.<br>Values must be in the range `[0.0, inf)`.<br><br>.. versionadded:: 0.20</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-ccp_alpha;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
       "            ccp_alpha\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-ccp_alpha;\">\n",
       "            ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed.<br>Values must be in the range `[0.0, inf)`.<br>See :ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-estimators_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=estimators_,-int\">\n",
       "            estimators_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-estimators_;\">\n",
       "            estimators_: ndarray of DecisionTreeRegressor of shape (n_estimators, 1)<br><br>The collection of fitted sub-estimators.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[object](91, 1)</td>\n",
       "           <td>[[DecisionTreeRegressor(max_depth=1, min_samples_leaf=9,\n",
       "                        random_state=RandomState(MT19937) at 0x2ACC9A97940)],\n",
       " [DecisionTreeRegressor(max_depth=1, min_samples_leaf=9,\n",
       "                        random_state=RandomState(MT19937) at 0x2ACC9A97940)],\n",
       " [DecisionTreeRegressor(max_depth=1, min_samples_leaf=9,\n",
       "                        random_state=RandomState(MT19937) at 0x2ACC9A97940)],\n",
       " ...,\n",
       " [DecisionTreeRegressor(max_depth=1, min_samples_leaf=9,\n",
       "                        random_state=RandomState(MT19937) at 0x2ACC9A97940)],\n",
       " [DecisionTreeRegressor(max_depth=1, min_samples_leaf=9,\n",
       "                        random_state=RandomState(MT19937) at 0x2ACC9A97940)],\n",
       " [DecisionTreeRegressor(max_depth=1, min_samples_leaf=9,\n",
       "                        random_state=RandomState(MT19937) at 0x2ACC9A97940)]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-feature_importances_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=feature_importances_,-ndarray%20of%20shape%20%28n_features%2C%29\">\n",
       "            feature_importances_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-feature_importances_;\">\n",
       "            feature_importances_: ndarray of shape (n_features,)<br><br>The impurity-based feature importances.<br>The higher, the more important the feature.<br>The importance of a feature is computed as the (normalized)<br>total reduction of the MSE brought by that feature.  It is also<br>known as the Gini importance.<br><br>Warning: impurity-based feature importances can be misleading for<br>high cardinality features (many unique values). See<br>:func:`sklearn.inspection.permutation_importance` as an alternative.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](10,)</td>\n",
       "           <td>[0.01,0.02,0.44,...,0.01,0.27,0.03]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-init_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=init_,-estimator\">\n",
       "            init_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-init_;\">\n",
       "            init_: estimator<br><br>The estimator that provides the initial predictions. Set via the ``init``<br>argument.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">DummyRegressor</td>\n",
       "           <td>DummyRegressor()</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_features_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=max_features_,-int\">\n",
       "            max_features_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_features_;\">\n",
       "            max_features_: int<br><br>The inferred value of max_features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>10</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_estimators_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=n_estimators_,-int\">\n",
       "            n_estimators_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_estimators_;\">\n",
       "            n_estimators_: int<br><br>The number of estimators as selected by early stopping (if<br>``n_iter_no_change`` is specified). Otherwise it is set to<br>``n_estimators``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>91</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>10</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_trees_per_iteration_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=n_trees_per_iteration_,-int\">\n",
       "            n_trees_per_iteration_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_trees_per_iteration_;\">\n",
       "            n_trees_per_iteration_: int<br><br>The number of trees that are built at each iteration. For regressors, this is<br>always 1.<br><br>.. versionadded:: 1.4.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>1</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-oob_improvement_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=oob_improvement_,-ndarray%20of%20shape%20%28n_estimators%2C%29\">\n",
       "            oob_improvement_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-oob_improvement_;\">\n",
       "            oob_improvement_: ndarray of shape (n_estimators,)<br><br>The improvement in loss on the out-of-bag samples<br>relative to the previous iteration.<br>``oob_improvement_[0]`` is the improvement in<br>loss of the first stage over the ``init`` estimator.<br>Only available if ``subsample &lt; 1.0``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](91,)</td>\n",
       "           <td>[  144.17, -875.67, -814.59,...,  771.33,-1014.01,  121.24]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-oob_score_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=oob_score_,-float\">\n",
       "            oob_score_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-oob_score_;\">\n",
       "            oob_score_: float<br><br>The last value of the loss on the out-of-bag samples. It is<br>the same as `oob_scores_[-1]`. Only available if `subsample &lt; 1.0`.<br><br>.. versionadded:: 1.3</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">float64</td>\n",
       "           <td>2950</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-oob_scores_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=oob_scores_,-ndarray%20of%20shape%20%28n_estimators%2C%29\">\n",
       "            oob_scores_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-oob_scores_;\">\n",
       "            oob_scores_: ndarray of shape (n_estimators,)<br><br>The full history of the loss values on the out-of-bag<br>samples. Only available if `subsample &lt; 1.0`.<br><br>.. versionadded:: 1.3</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](91,)</td>\n",
       "           <td>[5039.61,5915.28,6729.87,...,2057.01,3071.02,2949.78]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-train_score_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#:~:text=train_score_,-ndarray%20of%20shape%20%28n_estimators%2C%29\">\n",
       "            train_score_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-train_score_;\">\n",
       "            train_score_: ndarray of shape (n_estimators,)<br><br>The i-th score ``train_score_[i]`` is the loss of the<br>model at iteration ``i`` on the in-bag sample.<br>If ``subsample == 1`` this is the loss on the training data.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](91,)</td>\n",
       "           <td>[6152.26,5465.45,4819.29,...,2747.9 ,2356.48,2398.55]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div></div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
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       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-1');</script></body>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "id": "b00f6c28",
   "metadata": {},
   "source": [
    "## Evaluate Predictions\n",
    "\n",
    "`GASearchCV` refits the best pipeline, so you can call `predict` directly on the search object."
   ]
  },
  {
   "cell_type": "code",
   "id": "88c3a00b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-20T05:35:21.348878Z",
     "iopub.status.busy": "2026-06-20T05:35:21.348598Z",
     "iopub.status.idle": "2026-06-20T05:35:21.394662Z",
     "shell.execute_reply": "2026-06-20T05:35:21.393466Z"
    },
    "ExecuteTime": {
     "end_time": "2026-06-20T18:51:12.487413900Z",
     "start_time": "2026-06-20T18:51:12.469275600Z"
    }
   },
   "source": [
    "print(\"Best CV negative RMSE:\", round(search.best_score_, 4))\n",
    "print(\"Best parameters:\")\n",
    "pprint(search.best_params_)\n",
    "\n",
    "ga_metrics = regression_metrics(search, X_test, y_test)\n",
    "pd.DataFrame([baseline_metrics, ga_metrics], index=[\"baseline\", \"ga_pipeline\"])\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best CV negative RMSE: -58.8192\n",
      "Best parameters:\n",
      "{'regressor__learning_rate': 0.0875444183193989,\n",
      " 'regressor__loss': 'squared_error',\n",
      " 'regressor__max_depth': 1,\n",
      " 'regressor__min_samples_leaf': 9,\n",
      " 'regressor__n_estimators': 91,\n",
      " 'regressor__subsample': 0.7276569516060816}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                   r2       rmse        mae\n",
       "baseline     0.430319  55.455523  44.717961\n",
       "ga_pipeline  0.498775  52.017011  41.412206"
      ],
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r2</th>\n",
       "      <th>rmse</th>\n",
       "      <th>mae</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>baseline</th>\n",
       "      <td>0.430319</td>\n",
       "      <td>55.455523</td>\n",
       "      <td>44.717961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ga_pipeline</th>\n",
       "      <td>0.498775</td>\n",
       "      <td>52.017011</td>\n",
       "      <td>41.412206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "id": "d032a0e3",
   "metadata": {},
   "source": [
    "## Inspect Search Cost and Telemetry\n",
    "\n",
    "`fit_stats_` summarizes evaluation mechanics. `history` stores generation-level telemetry, including diversity and stagnation fields."
   ]
  },
  {
   "cell_type": "code",
   "id": "1f83f4ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-20T05:35:21.397724Z",
     "iopub.status.busy": "2026-06-20T05:35:21.397438Z",
     "iopub.status.idle": "2026-06-20T05:35:21.402831Z",
     "shell.execute_reply": "2026-06-20T05:35:21.401742Z"
    },
    "ExecuteTime": {
     "end_time": "2026-06-20T18:51:12.567935Z",
     "start_time": "2026-06-20T18:51:12.488414200Z"
    }
   },
   "source": [
    "search.fit_stats_"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'evaluated_candidates': 134,\n",
       " 'unique_candidates': 133,\n",
       " 'cross_validate_calls': 133,\n",
       " 'cache_hits': 1,\n",
       " 'duplicate_candidates': 0,\n",
       " 'skipped_invalid_candidates': 0,\n",
       " 'population_parallel_batches': 7,\n",
       " 'population_serial_batches': 0,\n",
       " 'random_immigrants': 3,\n",
       " 'local_refinement_candidates': 2}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "id": "0146dbfb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-20T05:35:21.405235Z",
     "iopub.status.busy": "2026-06-20T05:35:21.404863Z",
     "iopub.status.idle": "2026-06-20T05:35:21.426074Z",
     "shell.execute_reply": "2026-06-20T05:35:21.425118Z"
    },
    "ExecuteTime": {
     "end_time": "2026-06-20T18:51:12.712503600Z",
     "start_time": "2026-06-20T18:51:12.568935300Z"
    }
   },
   "source": [
    "history = pd.DataFrame(search.history)\n",
    "telemetry_columns = [\n",
    "    \"gen\",\n",
    "    \"fitness\",\n",
    "    \"fitness_max\",\n",
    "    \"fitness_std\",\n",
    "    \"unique_individual_ratio\",\n",
    "    \"genotype_diversity\",\n",
    "    \"stagnation_generations\",\n",
    "    \"best_generation\",\n",
    "]\n",
    "history[[column for column in telemetry_columns if column in history.columns]].tail()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   gen    fitness  fitness_max  fitness_std  unique_individual_ratio  \\\n",
       "1    1 -60.658669   -59.955816     0.461462                 0.666667   \n",
       "2    2 -60.718057   -58.921317     0.785379                 0.750000   \n",
       "3    3 -59.743591   -59.106876     0.587545                 0.666667   \n",
       "4    4 -59.913208   -59.106876     0.596168                 0.666667   \n",
       "5    5 -61.326108   -58.819157     1.757950                 0.750000   \n",
       "\n",
       "   genotype_diversity  stagnation_generations  best_generation  \n",
       "1            0.287879                       1                0  \n",
       "2            0.363636                       0                2  \n",
       "3            0.303030                       1                2  \n",
       "4            0.333333                       2                2  \n",
       "5            0.409091                       0                5  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gen</th>\n",
       "      <th>fitness</th>\n",
       "      <th>fitness_max</th>\n",
       "      <th>fitness_std</th>\n",
       "      <th>unique_individual_ratio</th>\n",
       "      <th>genotype_diversity</th>\n",
       "      <th>stagnation_generations</th>\n",
       "      <th>best_generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-60.658669</td>\n",
       "      <td>-59.955816</td>\n",
       "      <td>0.461462</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.287879</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-60.718057</td>\n",
       "      <td>-58.921317</td>\n",
       "      <td>0.785379</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-59.743591</td>\n",
       "      <td>-59.106876</td>\n",
       "      <td>0.587545</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.303030</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-59.913208</td>\n",
       "      <td>-59.106876</td>\n",
       "      <td>0.596168</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>-61.326108</td>\n",
       "      <td>-58.819157</td>\n",
       "      <td>1.757950</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "id": "aa43bd84",
   "metadata": {},
   "source": [
    "## Visualize the Search\n",
    "\n",
    "The plotting helpers work directly with fitted search objects. Use them for quick inspection, then rely on `history` and `cv_results_` when you need custom reporting."
   ]
  },
  {
   "cell_type": "code",
   "id": "06c36132",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-20T05:35:21.428677Z",
     "iopub.status.busy": "2026-06-20T05:35:21.428321Z",
     "iopub.status.idle": "2026-06-20T05:35:22.784855Z",
     "shell.execute_reply": "2026-06-20T05:35:22.783946Z"
    },
    "ExecuteTime": {
     "end_time": "2026-06-20T18:51:13.017246Z",
     "start_time": "2026-06-20T18:51:12.720587900Z"
    }
   },
   "source": [
    "plot_fitness_evolution(search)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Best fitness so far'}, xlabel='generations', ylabel='fitness (score)'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "id": "d1834a01",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-20T05:35:22.787363Z",
     "iopub.status.busy": "2026-06-20T05:35:22.787117Z",
     "iopub.status.idle": "2026-06-20T05:35:23.809901Z",
     "shell.execute_reply": "2026-06-20T05:35:23.808965Z"
    },
    "ExecuteTime": {
     "end_time": "2026-06-20T18:51:13.526665200Z",
     "start_time": "2026-06-20T18:51:13.018247600Z"
    }
   },
   "source": [
    "plot_search_space(search, features=[\"regressor__learning_rate\", \"regressor__max_depth\"])\n",
    "plot_search_space(search, features=[\"regressor__learning_rate\", \"regressor__max_depth\"])"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2accadc3770>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 400x400 with 6 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "id": "9ad7310a",
   "metadata": {},
   "source": [
    "## Practical Notes\n",
    "\n",
    "- Use pipeline parameter names exactly as sklearn expects them.\n",
    "- For regression losses where larger is better only after negation, use sklearn's negative scorers such as `neg_root_mean_squared_error`.\n",
    "- Compare holdout metrics, not only CV fitness.\n",
    "- If the search revisits many candidates, inspect `cache_hits` and consider stronger diversity controls or a larger search space."
   ]
  }
 ],
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