scikit-learn models hyperparameters tuning and feature selection, using evolutionary algorithms.

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


Install sklearn-genetic-opt

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

sklearn-genetic-opt requires:

  • Python (>= 3.7)

  • scikit-learn (>= 0.21.3)

  • NumPy (>= 1.14.5)

  • DEAP (>= 1.3.1)

  • tqdm (>= 4.61.1)

Extra requirements:

These requirements are necessary to use plots, MLflowConfig and TensorBoard correspondingly.

  • 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]

External References:

Indices and tables