ELI5 is a Python library designed to help debug machine learning classifiers and explain their predictions. It targets data scientists and ML engineers working with various frameworks, offering insights into model behavior and facilitating easier debugging. The primary benefit is enhanced model interpretability and faster identification of issues.

How It Works

ELI5 provides a unified interface for explaining model internals and predictions across multiple ML libraries. It supports direct inspection of weights and feature importances for linear models, decision trees, and tree ensembles from scikit-learn, XGBoost, LightGBM, CatBoost, and lightning. For black-box models, it integrates LIME for text explanations and offers permutation importance. Explanations can be rendered as text, HTML, pandas DataFrames, or JSON for flexible integration.

Quick Start & Requirements

• Install via pip: pip install eli5
• Requires Python. Supports various ML frameworks including scikit-learn, Keras, XGBoost, LightGBM, CatBoost, lightning, sklearn-crfsuite, and OpenAI Python client.
• Documentation available at: https://eli5.readthedocs.io/

Highlighted Details

• Supports explanation of scikit-learn pipelines and FeatureUnions.
• Can debug HashingVectorizer issues by undoing hashing.
• Offers Grad-CAM visualizations for Keras image classifiers.
• Provides token probability explanations for OpenAI LLM predictions.

Maintenance & Community

• Previously developed at https://github.com/TeamHG-Memex/eli5 with support from Hyperion Gray.
• Active GitHub repository with CI/CD build status and code coverage reporting.

Licensing & Compatibility

• License: MIT.
• Compatible with commercial and closed-source projects.

Limitations & Caveats

The library's support for non-text data and arbitrary black-box classifiers using LIME is noted as experimental.