{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"Explains predictions and weights of machine learning classifiers from scikit-learn, XGBoost, LightGBM, CatBoost, Keras, and other frameworks, with support for text highlighting, feature importance visualization, and black-box model inspection via LIME and permutation importance.","skillfed_tags":["model-interpretability","explainability","debugging"],"use_cases":["Debug linear classifiers and regressors by examining learned weights and prediction contributions.","Visualize feature importances and decision paths for tree-based ensembles.","Explain text classifier predictions by highlighting which words or features influenced the decision.","Inspect black-box model behavior using LIME for arbitrary classifiers or permutation importance.","Generate Grad-CAM visualizations for Keras image classifiers to understand prediction drivers.","Undo hashing in pipelines containing HashingVectorizer to recover interpretable feature names."],"what_it_does":"ELI5 is a Python package for debugging and explaining machine learning model predictions. It provides native support for scikit-learn, XGBoost, LightGBM, CatBoost, Keras, and other frameworks, allowing you to inspect model weights, visualize feature importances, print decision trees as text or SVG, and highlight text features used in predictions. For black-box models, it implements LIME-based text explanation and permutation importance methods.\n\nExplanations can be rendered as text for console output, HTML for notebooks and dashboards, pandas DataFrames for further processing, or JSON for custom client-side rendering. The package is built on attrs, jinja2, numpy, scipy, scikit-learn, graphviz, and tabulate, making it straightforward to integrate into existing ML workflows.","worth_installing":"Yes, with conditions. Install if you need model explainability for scikit-learn or gradient-boosted models in a research or production debugging context. The low install friction and permissive license make it a low-risk addition. However, the aging maintenance status (481 days since last release) means you should verify that the framework versions you use are compatible; test thoroughly before relying on it in critical pipelines."},"id":"eli5","links":{"html":"https://skillfed.io/packages/eli5","md":"https://skillfed.io/packages/eli5.md","pypi":"https://pypi.org/project/eli5/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-04-20","license_spdx":null,"license_treatment":"permissive","name":"eli5","python_support":"supports_current","summary":"Debug machine learning classifiers and explain their predictions"},"popularity":{"monthly_downloads":137234,"position":11374,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.16.0"}