Explainable Ai Python Packages

interpret-core

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.

interpret

Fit interpretable models. Explain blackbox machine learning.

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.

dalex

moDel Agnostic Language for Exploration and eXplanation

imodels

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

flash-ansr

Fast Amortized Neural Symbolic Regression with Transformers and SimpliPy

shapiq

Shapley Interactions and Shapley Values for Machine Learning

dice-ml

Generate Diverse Counterfactual Explanations for any machine learning model.

fasttreeshap

Fast SHAP value computation for interpreting tree-based models

trustgraph-base

The Semantic Intelligence Layer for Ontologies

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.

semantica

Graph-Native Infrastructure for Context and Accountable AI Systems

trustgraph-embeddings-hf

The Semantic Intelligence Layer for Ontologies

trainlens

TrainLens turns training runs into evidence: what changed, what went wrong, and what experiment to run next.

trustgraph-flow

The Semantic Intelligence Layer for Ontologies

trustgraph-cli

The Semantic Intelligence Layer for Ontologies

trustgraph

The Semantic Intelligence Layer for Ontologies

trustgraph-vertexai

The Semantic Intelligence Layer for Ontologies