scikit-learn models hyperparameters tuning and feature selection, using evolutionary algorithms.
This is meant to be an alternative to popular methods inside scikit-learn such as Grid Search and Randomized Grid Search for hyperparameters tuning, and from RFE, Select From Model for feature selection.
Sklearn-genetic-opt uses evolutionary algorithms from the deap package to choose a set of hyperparameters that optimizes (max or min) the cross-validation scores, it can be used for both regression and classification problems.
It’s advised to install sklearn-genetic using a virtual env, to install a light version, inside the env use:
pip install sklearn-genetic-opt
Python (>= 3.7)
scikit-learn (>= 0.21.3)
NumPy (>= 1.14.5)
DEAP (>= 1.3.1)
tqdm (>= 4.61.1)
Seaborn (>= 0.9.0)
MLflow (>= 1.17.0)
Tensorflow (>= 2.0.0)
This command will install all the extra requirements, except for Tensorflow, as it is usually advised to look further which distribution works better for you:
pip install sklearn-genetic-opt[all]