{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"Powershap performs feature selection by testing whether each feature's Shapley values are statistically significantly larger than a random feature's, using hypothesis testing and automatic power calculations to determine the required iterations.","skillfed_tags":["feature-selection","interpretability","statistical-testing"],"use_cases":["Reduce dataset dimensionality before training a production model by identifying statistically significant predictive features.","Compare feature importance across different model types to find consensus on which features matter most.","Validate domain expertise by testing whether known important features rank above random noise in a statistical test.","Automate feature selection in pipelines without manual threshold tuning, using the automatic mode's power-based iteration scheduling."],"what_it_does":"Powershap is a feature selection method that combines Shapley value analysis with statistical hypothesis testing to identify which features are genuinely informative. It works by training multiple models on different data subsets, each time adding a random uniform feature as a baseline. For each feature, it calculates mean absolute Shapley values across iterations and compares them statistically to the random feature's impact using a percentile-based p-value test. Features with p-values below a threshold (default 0.01) are selected as significant.\n\nThe package includes an automatic mode that avoids manual hyperparameter tuning by using effect size and statistical power calculations to determine how many iterations are needed to achieve a target power level (default 0.99). It supports various model types\u2014linear, tree-based, and deep learning\u2014for both classification and regression, and integrates with scikit-learn conventions. The five runtime dependencies (catboost, pandas, scikit-learn, shap, statsmodels) are standard data science libraries.","worth_installing":"Yes, with conditions. Powershap is a solid choice if you need statistically grounded feature selection and can tolerate aging maintenance (last update 323 days ago, no recent commits). The automatic mode removes hyperparameter tuning friction, and it integrates well with scikit-learn workflows. However, verify that its power-calculation defaults and statistical assumptions fit your problem domain before relying on it for critical feature selection decisions."},"id":"powershap","links":{"html":"https://skillfed.io/packages/powershap","md":"https://skillfed.io/packages/powershap.md","pypi":"https://pypi.org/project/powershap/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-09-25","license_spdx":"MIT","license_treatment":"permissive","name":"powershap","python_support":"supports_current","summary":"Feature selection using statistical significance of shap values"},"popularity":{"monthly_downloads":117711,"position":12151,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.0.1"}