{"library":"skrebate","title":"skrebate","description":"A scikit-learn-compatible implementation of Relief-based feature selection algorithms for classification and regression. Current version 0.62; development is infrequent (last release 2020).","language":"python","status":"maintenance","last_verified":"Fri May 01","install":{"commands":["pip install skrebate"],"cli":null},"imports":["from skrebate import ReliefF","from skrebate import SURF","from skrebate import MultiSURF"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"from skrebate import ReliefF\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\n\nX, y = load_iris(return_X_y=True)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Feature selection with ReliefF\nfs = ReliefF(n_neighbors=10, n_features_to_select=2)\nfs.fit(X_train, y_train)\nX_train_selected = fs.transform(X_train)\nX_test_selected = fs.transform(X_test)\nprint(X_train_selected.shape)\n","lang":"python","description":"Basic ReliefF feature selection on the Iris dataset","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}