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.
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-optsklearn-genetic-opt requires:
- Python (>= 3.8)
- scikit-learn (>= 1.1.0)
- NumPy (>= 1.19.0)
- DEAP (>= 1.3.3)
- tqdm (>= 4.61.1)
Extra requirements:
These requirements are necessary to use ~sklearn_genetic.plots, ~sklearn_genetic.mlflow.MLflowConfig and ~sklearn_genetic.callbacks.TensorBoard correspondingly.
- Seaborn (>= 0.11.2)
- MLflow (>= 1.30.0)
- Tensorflow (>= 2.0.0)
This command will install all the extra requirements:
pip install sklearn-genetic-opt[all]tutorials/basic_usage tutorials/callbacks tutorials/custom_callback tutorials/adapters tutorials/understand_cv tutorials/mlflow tutorials/reproducibility
notebooks/sklearn_comparison.ipynb notebooks/Pipeline_prediction.ipynb notebooks/Iris_feature_selection.ipynb notebooks/Digits_decision_tree.ipynb notebooks/MLflow_logger.ipynb notebooks/Iris_multimetric.ipynb
release_notes
api/gasearchcv api/gafeatureselectioncv api/callbacks api/schedules api/plots api/mlflow api/space api/algorithms
external_references
genindexmodindexsearch