sklean-genetic-opt¶

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2021-06-25 23:00:06

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 choose 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) MLflow (>= 1.17.0) User Guide / Tutorials: How to Use Sklearn-genetic-opt Introduction Example Using Callbacks Introduction ConsecutiveStopping DeltaThreshold ThresholdStopping LogbookSaver Define Multiple Callbacks Full Example Custom Callbacks Understanding the evaluation process Parameters Steps Example Integrating with MLflow Configuration Example Release Notes What’s new in 0.5.0 What’s new in 0.4.1 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 Plots MLflow Space Algorithms External References: Articles

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 choose set of hyperparameters that optimizes (max or min) the cross validation scores, it can be used for both regression and classification problems.