sklean-genetic-opt

scikit-learn models hyperparameters tuning, using evolutionary algorithms.

This is meant to be an alternative from popular methods inside scikit-learn such as Grid Search and Randomized Grid Search.

Sklearn-genetic-opt uses evolutionary algorithms from the deap package to find the “best” set of hyperparameters that optimizes (max or min) the cross validation scores, it can be used for both regression and classification problems.

Installation:

Install sklearn-genetic-opt

It’s advised to install sklearn-genetic using a virtual env, inside the env use:

pip install sklearn-genetic-opt

sklearn-genetic-opt requires:

• Python (>= 3.7)

• scikit-learn (>= 0.21.3)

• NumPy (>= 1.14.5)

• Seaborn (>= 0.9.0)

• DEAP (>= 1.3.1)

• Pydantic (>= 1.8.2)

User Guide / Tutorials:

• How to Use Sklearn-genetic-opt
  • Introduction
  • Example
• Using Callbacks
  • Introduction
  • ConsecutiveStopping
  • DeltaThreshold
  • ThresholdStopping
  • Define Multiple Callbacks
  • Full Example
• Custom Callbacks
• Release Notes
  • What’s new in 0.4
  • What’s new in 0.3
  • What’s new in 0.2
  • What’s new in 0.1

API Reference:

• GASearchCV
• Callbacks
• Space
• Algorithms
• Plots

Indices and tables

• Index

• Module Index

• Search Page