{"library":"shap-hypetune","title":"shap-hypetune","description":"A Python package for simultaneous hyperparameters tuning and feature selection for gradient boosting models (LightGBM, XGBoost, CatBoost, Random Forest). Current version: 0.2.7. Release cadence: occasional, last release Jun 2023.","language":"python","status":"active","last_verified":"Fri May 01","install":{"commands":["pip install shap-hypetune"],"cli":null},"imports":["from shap_hypetune import BoostingRFA","from shap_hypetune import BoostingRFE","from shap_hypetune import BoostingRFECV","from shap_hypetune import BoostingSelector"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import pandas as pd\nfrom sklearn.datasets import make_classification\nfrom sklearn.model_selection import train_test_split\nfrom shap_hypetune import BoostingRFA\n\nX, y = make_classification(n_samples=100, n_features=20, random_state=0)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n\nmodel = BoostingRFA(estimator='lgbm', n_iter=5, early_stopping_rounds=10, random_state=0)\nmodel.fit(X_train, y_train)\nprint('Train accuracy:', model.score(X_train, y_train))\nprint('Selected features:', model.get_support())\n","lang":"python","description":"Basic usage: perform recursive feature addition with hyperparameter tuning using LightGBM. Note: estimator can be 'lgbm', 'xgb', 'catboost', or 'rf' (Random Forest, added in v0.2.4).","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}