{"library":"powershap","title":"powershap","description":"Powerful feature selection using statistical significance of SHAP values. Current version: 0.1.0.1. Active development, major releases every few months.","language":"python","status":"active","last_verified":"Fri May 01","install":{"commands":["pip install powershap"],"cli":null},"imports":["from powershap import PowerShap"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"from powershap import PowerShap\nfrom sklearn.datasets import make_classification\nfrom sklearn.ensemble import RandomForestClassifier\nimport pandas as pd\n\nX, y = make_classification(n_samples=100, n_features=20, random_state=42)\nX = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(X.shape[1])])\n\nselector = PowerShap(\n    model=RandomForestClassifier(),\n    power_analysis='auto',       # automatic estimation\n    cv=5,\n    random_state=42\n)\nselector.fit(X, y)\nprint('Selected features:', selector.transform(X).columns.tolist())","lang":"python","description":"Quickstart: fit a PowerShap selector on a classification dataset and get selected features.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}