{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"Trains interpretable machine learning models and explains blackbox predictions using techniques like Explainable Boosting Machines, SHAP, LIME, and decision trees.","skillfed_tags":["model-interpretability","explainable-ai","fairness-detection"],"use_cases":["Train an Explainable Boosting Machine for classification or regression when you need both accuracy and full model transparency.","Debug why a blackbox model made a specific prediction using SHAP or LIME explainers on existing models.","Detect fairness issues by analyzing global feature importance and local prediction explanations across demographic groups.","Satisfy regulatory requirements in finance or healthcare by producing exact, auditable explanations for individual decisions.","Engineer better features by understanding which variables drive model predictions and how they interact."],"what_it_does":"interpret-core is a unified framework for training interpretable models and explaining blackbox machine learning systems. It implements state-of-the-art techniques including Explainable Boosting Machines (EBMs)\u2014a modern take on Generalized Additive Models that achieve accuracy comparable to random forests and gradient boosting while remaining fully interpretable\u2014alongside explainers for existing models like SHAP, LIME, and partial dependence analysis. The package is built on numpy, pandas, scikit-learn, and joblib, making it a natural fit for standard data science workflows.\n\ninterpret-core addresses critical use cases in model debugging, feature engineering, fairness detection, regulatory compliance, and high-risk domains like healthcare and finance. You can train glassbox models directly, explain individual predictions, compare multiple models side-by-side, and optionally apply differential privacy for sensitive data. The framework handles pandas DataFrames and numpy arrays natively and includes support for pairwise interactions by default.","worth_installing":"Yes. interpret-core is actively maintained, has low install friction, and fills a genuine need for interpretable ML in regulated and high-stakes domains. The license treatment is unclear\u2014verify the actual license before use in proprietary contexts\u2014but the package itself is stable and has no known vulnerabilities. Install if you need interpretability alongside accuracy."},"id":"interpret-core","links":{"html":"https://skillfed.io/packages/interpret-core","md":"https://skillfed.io/packages/interpret-core.md","pypi":"https://pypi.org/project/interpret-core/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-17","license_spdx":null,"license_treatment":"unclear","name":"interpret-core","python_support":"unspecified","summary":"Fit interpretable models. Explain blackbox machine learning."},"popularity":{"monthly_downloads":966463,"position":4618,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.7.8"}