{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/11"}],"enrichment":{"capability":"Skope-rules learns interpretable logical rules from data to classify instances with high precision, balancing the simplicity of decision trees with the power of random forests.","skillfed_tags":["interpretable-ml","rule-extraction","ensemble-methods"],"use_cases":["Extract human-readable classification rules from a trained tree ensemble to understand which feature combinations define a target class.","Build a high-precision detector for rare or critical events where false positives are costly and interpretability is required.","Score new instances by the precision of the rules that match them, balancing accuracy with explainability.","Generate a small set of actionable business rules from historical data for manual review and deployment in rule engines.","Compare rule-based predictions with tree or forest predictions to validate that simplified rules capture ensemble logic."],"what_it_does":"Skope-rules is a machine learning module built on top of scikit-learn that learns interpretable logical rules for binary classification by extracting and deduplicating rules from tree ensembles. It aims to find rules that detect instances of a target class with high precision, offering a middle ground between the transparency of a single decision tree and the predictive power of a random forest.\n\nThe package extracts rules from tree ensembles, removes near-duplicates based on support similarity, and filters them by user-specified precision and recall thresholds. Rules can be used both for explanation (examining the logical conditions) and for prediction via methods that rank instances by the precision of the rules that match them.","worth_installing":"Yes, if you need interpretable rules for binary classification and can accept dormant maintenance. The package is stable, has low install friction, and carries no known vulnerabilities. However, verify compatibility with your current numpy, scikit-learn, scipy, and pandas versions before relying on it in production."},"id":"skope-rules","links":{"html":"https://skillfed.io/packages/skope-rules","md":"https://skillfed.io/packages/skope-rules.md","pypi":"https://pypi.org/project/skope-rules/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2020-01-25","license_spdx":null,"license_treatment":"permissive","name":"skope-rules","python_support":"unspecified","summary":"Machine Learning with Interpretable Rules"},"popularity":{"monthly_downloads":86577,"position":13851,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.1"}