Explainable Ai Python Packages

interpret-core

Fit interpretable models. Explain blackbox machine learning.

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.

sahi

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

traceml

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

datatile

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

pandas-summary

Engine for AI/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

trustgraph-embeddings-hf

Write context once. Run agents anywhere. Own your data and the 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.

fasttreeshap

Fast SHAP value computation for interpreting tree-based models

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.

trustgraph-base

Write context once. Run agents anywhere. Own your data and the models.

trustgraph-cli

Write context once. Run agents anywhere. Own your data and the models.

gamma-facet

Human-explainable AI.

trustgraph-vertexai

Write context once. Run agents anywhere. Own your data and the models.

trustgraph-flow

Write context once. Run agents anywhere. Own your data and the models.

trustgraph-ocr

Write context once. Run agents anywhere. Own your data and the models.

trustgraph

Write context once. Run agents anywhere. Own your data and the models.