Selective: Feature Selection Library
Like fidelity/selective, 0 likes 0 Bookmark fidelity/selective, 0 bookmarks 0 A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.
Like EpistasisLab/scikit-rebate, 0 likes 0 Bookmark EpistasisLab/scikit-rebate, 0 bookmarks 0 Feature engineering and selection open-source Python library compatible with sklearn.
Like feature-engine/feature_engine, 0 likes 0 Bookmark feature-engine/feature_engine, 0 bookmarks 0 LinearBoost Classifier is a rapid and accurate classification algorithm that builds upon a very fast, linear classifier.
Like LinearBoost/linearboost-classifier, 0 likes 0 Bookmark LinearBoost/linearboost-classifier, 0 bookmarks 0 scikit-learn contrib estimators
Like scikit-learn-contrib/scikit-learn-extra, 0 likes 0 Bookmark scikit-learn-contrib/scikit-learn-extra, 0 bookmarks 0 SHAP-based validation for linear and tree-based models. Applied to binary, multiclass and regression problems.
Like ing-bank/probatus, 0 likes 0 Bookmark ing-bank/probatus, 0 bookmarks 0 Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.
Like rodrigo-arenas/Sklearn-genetic-opt, 0 likes 0 Bookmark rodrigo-arenas/Sklearn-genetic-opt, 0 bookmarks 0 Library for Semi-Automated Data Science
Like IBM/lale, 0 likes 0 Bookmark IBM/lale, 0 bookmarks 0 A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Like catboost/catboost, 0 likes 0 Bookmark catboost/catboost, 0 bookmarks 0 
