Search Packages

shap

A game theoretic approach to explain the output of any machine learning model.

powershap

A power-full Shapley feature selection method.

explainerdashboard

Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

fasttreeshap

Fast SHAP value computation for interpreting tree-based models

shapiq

Shapley Interactions and Shapley Values for Machine Learning

probatus

SHAP-based validation for linear and tree-based models. Applied to binary, multiclass and regression problems.

shapash

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

shap-hypetune

A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.

streamlit-shap

streamlit-shap provides a wrapper to display SHAP plots in Streamlit.

xper

A methodology designed to measure the contribution of the features to the predictive performance of any econometric or machine learning model.

shaperone

Shaperone is a fork of the SHAP library, fixing open issues to improve usability.

timeshap

TimeSHAP explains Recurrent Neural Network predictions.

atom-ml

Automated Tool for Optimized Modelling

spml2-mltools

spml2-mltools is a convenient package for applying machine learning workflows with a Streamlit app, generating Excel outputs, and visualizations.

pandasflow

A set of custom python modules for friendly workflow on pandas

shap-monitor

Monitor and explain your ML model in production

dissectml

The missing middle layer between EDA and AutoML — unified pipeline from deep data understanding to model comparison in 3 function calls. Deep EDA, 36-model battle arena, statistical significance testing, target leakage detection, and publication-ready HTML reports.

finreg-ml

Regulation-aware ML pipeline for finance

shap-relativities

Extract multiplicative rating relativities from GBM models using SHAP values. Built for insurance pricing.

survshap

SurvSHAP(t): Time-dependent explanations of machine learning survival models

insurance-glm-tools

GLM tooling for insurance pricing — nested GLM embeddings, R2VF factor level clustering, territory banding, SKATER

striders

Efficient surrogate-based model explanations (XAI) using landmark-based kernel approximations for scalable SHAP values.

insurance-interactions

Automated GLM interaction detection for UK personal lines insurance using CANN + NID

datatypical

Scientific Data Significance Rankings with Shapley Explanations