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

fasttreeshap

Fast SHAP value computation for interpreting tree-based models

shapiq

Shapley Interactions and Shapley Values for Machine Learning

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.

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.

tf-keras-vis

Neural network visualization toolkit for tf.keras

gamma-facet

Human-explainable AI.

xaisuite

XAISuite: Train machine learning models, generate explanations, and compare different explanation systems with just a simple line of code.

zennit

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

obliquetree

obliquetree is an advanced decision tree implementation featuring oblique and axis-aligned splits, optimized performance.

ruleopt

Optimization-Based Rule Learning for Classification

holisticai

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