interpretml/interpret

Features

• Glassbox Models - Implements inherently transparent glassbox models that provide exact and verifiable decision logic.
• Glassbox Model Construction - Enables the construction of inherently transparent glassbox models that provide exact explanations for every prediction.
• Differentially Private ML Libraries - Provides a framework for training interpretable models that use differential privacy to protect sensitive data.
• Feature Importance Attribution - Provides methods for quantifying the relative contribution of individual input variables to model predictions.
• Feature Interaction Analyzers - Includes tools to compute and analyze pairwise and higher-order dependencies between input features.
• Interpretable ML Libraries - Provides a collection of glassbox models and post-hoc explanation techniques to make predictions human-understandable.
• Model Interpretability - Applies mathematical techniques to explain the reasoning and feature contributions behind complex model predictions.
• Interpretable Model Training - Provides frameworks for training models that maintain inherent transparency and human-understandable decision logic.
• Model Explainability - Caclulates feature importance and detects feature interactions to reveal the reasoning behind complex decisions.
• Glassbox Frameworks - Ships a system for training inherently transparent models that provide exact and verifiable explanations.
• Privacy-Preserving Training - Trains transparent models using differential privacy to prevent sensitive data from leaking through explanations.
• Tabular Model Explanation - Generates global insights and local explanations for how features contribute to predictions in tabular datasets.
• Local and Global Explanations - Generates both individual prediction justifications and overall model behavior summaries to reveal underlying logic.
• Differentially Private Training - Integrates differential privacy into the training of interpretable models to prevent sensitive data leakage.
• Post-Hoc Approximations - Uses mathematical techniques to create simplified, transparent models that mimic complex black-box systems.
• ML Visualization Libraries - Provides interactive visual representations of model behavior and decision paths for high-dimensional data analysis.
• Model Auditing - Analyzes model behavior and exports structures to JSON to ensure fairness, consistency, and regulatory compliance.
• Model Interpretability Tools - Implements post-hoc techniques to approximate how non-interpretable models arrive at predictions.
• Model Explanation Visualizations - Renders interactive visual decision paths and model behavior to make complex reasoning human-readable.
• Complex Prediction Explanations - Generates local explanations and feature contributions to reveal why a complex model made a specific prediction.
• Post-Hoc Prediction Explanations - Describes why a non-interpretable model made a specific prediction using mathematical approximations.
• Feature Interaction Visualizations - Renders decision paths and feature interactions as interactive visual elements for human-readable model reasoning.
• Explainability and Fairness - Package for training interpretable models and explaining systems.
• Explainable AI Libraries - Toolkit for training interpretable models and explaining black-box systems.
• General Machine Learning - Library for explainable machine learning models.
• Machine Learning - Interpretable models and blackbox explanation.
• Model Interpretability - Toolkit for building interpretable and responsible machine learning models.
• Model Interpretation - Tools for training and explaining interpretable machine learning models.
• Guardrails and AI Safety - Listed in the “Guardrails and AI Safety” section of the The Incredible Pytorch awesome list.

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