Explainable Ml Python Packages

interpret-core

Fit interpretable models. Explain blackbox machine learning.

interpret

Fit interpretable models. Explain blackbox machine learning.

trulens-core

Evaluation and Tracking for LLM Experiments and AI Agents

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.

trulens-otel-semconv

Evaluation and Tracking for LLM Experiments and AI Agents

trulens-feedback

Evaluation and Tracking for LLM Experiments and AI Agents

trulens-dashboard

Evaluation and Tracking for LLM Experiments and AI Agents

imodels

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

trulens-eval

Evaluation and Tracking for LLM Experiments and AI Agents

dice-ml

Generate Diverse Counterfactual Explanations for any machine learning model.

trulens

Evaluation and Tracking for LLM Experiments and AI Agents

trulens-connectors-snowflake

Evaluation and Tracking for LLM Experiments and AI Agents

dalex

moDel Agnostic Language for Exploration and eXplanation

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.

trulens-providers-cortex

Evaluation and Tracking for LLM Experiments and AI Agents

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-providers-openai

Evaluation and Tracking for LLM Experiments and AI Agents

trulens-providers-litellm

Evaluation and Tracking for LLM Experiments and AI Agents

acv-dev

ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any model or data and different Shapley Values for tree-based models.

trulens-apps-langchain

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

shapash

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