interpretml/interpret
Interpret
Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training inherently transparent models and applying post-hoc explanation techniques to make machine learning predictions human-understandable.
The framework distinguishes itself by integrating differential privacy into the training of interpretable models to prevent sensitive data from leaking through explanations. It also features a visualization tool for rendering interactive decision paths and model behavior.
The library covers model explainability through feature importance calculation, interaction detection, and the generation of local and global explanations. It includes capabilities for auditing models via JSON serialization, enforcing monotonicity constraints, and approximating black-box systems.
The toolkit supports model management utilities such as model aggregation, merging, and the editing of trained model components.
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 - Fit interpretable models and explain 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.
