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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 calcu
Interpretable ML package π for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
π Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
The main features of maif/shapash are: Explainability and Fairness, General Machine Learning, Model Interpretability.
Open-source alternatives to maif/shapash include: interpretml/interpret β Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for trainingβ¦ pytorch/captum β Captum is an open-source library for explaining model predictions by attributing them to input features, neurons, andβ¦ csinva/imodels β Interpretable ML package π for concise, transparent, and accurate predictive modeling (sklearn-compatible). mljar/mljar-supervised β Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations andβ¦ aerdem4/lofo-importance β Leave One Feature Out Importance. seldonio/alibi β Algorithms for explaining machine learning models.
