Explainable Ai Python Packages

interpret-core

Fit interpretable models. Explain blackbox machine learning.

sahi

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

interpret

Fit interpretable models. Explain blackbox machine learning.

aplr

APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.

traceml

Engine for ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.

pandas-summary

Engine for ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.

datatile

Engine for ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.

grad-cam

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

raiutils

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

imodels

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

dice-ml

Generate Diverse Counterfactual Explanations for any machine learning model.

dalex

moDel Agnostic Language for Exploration and eXplanation

shapiq

Shapley Interactions and Shapley Values for Machine Learning

fasttreeshap

Fast SHAP value computation for interpreting tree-based models

erroranalysis

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

responsibleai

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

tf-keras-vis

Neural network visualization toolkit for tf.keras

raiwidgets

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

gamma-facet

Human-explainable AI.

zennit

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

xaisuite

XAISuite: Training and Explanation Generation Utilities for Machine Learning Models

holisticai

This is an open-source tool to assess and improve the trustworthiness of AI systems.

acfx

Actionable Counterfactual eXplanations

lofo-importance

Leave One Feature Out Importance