TeamHG-Memex/eli5

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2798 stars · 324 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

ELI5 is a Python library for debugging, inspecting, and explaining machine learning classifiers and regressors. It supports scikit-learn, XGBoost, LightGBM, CatBoost, Keras, and black-box models via LIME and permutation importance, with text, HTML, DataFrame, and JSON output formats.

Use cases

• explain predictions of a scikit-learn classifier
• show feature importances of an xgboost model
• visualize which words influenced a text classifier's prediction
• explain image classifier predictions with grad-cam
• compute permutation importance for a black-box estimator
• print a decision tree as text or svg
• debug a scikit-learn pipeline with hashing vectorizer

When to choose

• you need to interpret or debug predictions from scikit-learn, xgboost, lightgbm, or catboost models
• you want LIME-based explanations for arbitrary text classifiers
• you need explanation output as HTML for notebooks or JSON for custom rendering

When to avoid

• you need explanations for modern deep learning models beyond Keras image classifiers
• you require actively developed tooling with support for the latest model types
• you need model-agnostic explainers like SHAP with theoretical guarantees

Facets

library · maturity maintenance

machine-learning nlp data-science machine-learning data-science python explainability interpretability lime scikit-learn xgboost lightgbm feature-importance grad-cam natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="TeamHG-Memex/eli5")