{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"Boruta performs all-relevant feature selection by identifying all features carrying predictive information, using ensemble methods with a scikit-learn-compatible interface.","skillfed_tags":["feature-selection","ensemble-learning"],"use_cases":["Exploratory data analysis: identify all features related to your target to understand which factors influence your phenomenon.","Biological or medical data: use relaxed thresholds (perc parameter) when Bonferroni correction is too harsh for your domain.","Feature engineering validation: rank candidate features to guide which ones to engineer further or combine.","Preprocessing before minimal-optimal methods: run Boruta first to reduce noise, then apply stricter selectors.","Interpretability: obtain feature rankings and support masks to explain model decisions to stakeholders."],"what_it_does":"Boruta is a Python implementation of an all-relevant feature selection algorithm that identifies every feature carrying information useful for prediction, rather than finding a minimal optimal subset. It wraps ensemble methods from scikit-learn and uses a shadow-feature comparison approach with statistical testing to rank and select features. Unlike minimal-optimal methods that depend on classifier choice, Boruta aims to discover all contributing factors to your target variable, making it useful for exploratory analysis and understanding data relationships.\n\nThe package provides a scikit-learn-compatible interface (fit, transform, fit_transform) and includes refinements over the original R implementation: automatic estimator selection, feature ranking, percentile-based thresholds instead of strict maximums, and a two-step multiple-testing correction. It depends on numpy, scipy, and scikit-learn, making installation straightforward.","worth_installing":"Yes, with conditions. Boruta is well-established (1625 GitHub stars, 258914 monthly downloads) and has no known vulnerabilities. Install it if you need all-relevant feature selection with a scikit-learn-compatible interface and can accept aging maintenance (last release 2024-08-13). Not suitable if you require active, frequent updates or the latest algorithmic refinements."},"id":"boruta","links":{"html":"https://skillfed.io/packages/boruta","md":"https://skillfed.io/packages/boruta.md","pypi":"https://pypi.org/project/boruta/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2024-08-13","license_spdx":null,"license_treatment":"permissive","name":"Boruta","python_support":"unspecified","summary":"Python Implementation of Boruta Feature Selection"},"popularity":{"monthly_downloads":258914,"position":8422,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.4.3"}