Explainable Ml Python Packages

interpret-core

Fit interpretable models. Explain blackbox machine learning.

interpret

Fit interpretable models. Explain blackbox machine learning.

grad-cam

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

trulens-core

Evaluation and Tracking for LLM Experiments and AI Agents

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).

trulens-dashboard

Evaluation and Tracking for LLM Experiments and AI Agents

trulens-eval

Evaluation and Tracking for LLM Experiments and AI Agents

trulens-feedback

Evaluation and Tracking for LLM Experiments and AI Agents

dice-ml

Generate Diverse Counterfactual Explanations for any machine learning model.

trulens-otel-semconv

Evaluation and Tracking for LLM Experiments and AI Agents

dalex

moDel Agnostic Language for Exploration and eXplanation

trulens

Evaluation and Tracking for LLM Experiments and AI Agents

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.

trulens-connectors-snowflake

Evaluation and Tracking for LLM Experiments and AI Agents

trulens-providers-openai

Evaluation and Tracking for LLM Experiments and AI Agents

trulens-apps-langchain

Evaluation and Tracking for LLM Experiments and AI Agents

trulens-providers-litellm

Evaluation and Tracking for LLM Experiments and AI Agents

trulens-providers-cortex

Evaluation and Tracking for LLM Experiments and AI Agents

shapash

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

trulens-apps-llamaindex

Evaluation and Tracking for LLM Experiments and AI Agents

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