Which models does the SHAP skill support?

It supports tree-based models like XGBoost and LightGBM, deep learning frameworks like TensorFlow and PyTorch, linear models, and any black-box model via KernelExplainer.

How does SHAP help with model fairness?

It allows you to compare feature importance across different demographic groups and identify protected attributes or proxy features that may be introducing bias into your model.

What visualizations can this skill help generate?

This skill provides guidance for creating beeswarm plots for global importance, waterfall and force plots for individual predictions, and scatter plots for feature interactions.

What is the benefit of SHAP over standard feature importance?

SHAP provides theoretically grounded, additive, and consistent explanations that tell you exactly how much each feature pushes a specific prediction away from the baseline.

Can this skill handle large-scale datasets?

Yes, it includes performance optimization strategies such as using representative background data samples, kmeans summarization, and batching to maintain speed on large datasets.

SHAP Model Interpretability

Name: SHAP Model Interpretability
Author: Sologa

bySologa

Data Science & ML

Explains machine learning model predictions and feature importance using Shapley Additive exPlanations for transparent AI.

About

The SHAP skill provides a comprehensive framework for model interpretability and explainability, leveraging game theory-based Shapley values to demystify machine learning outputs. It offers specialized guidance for tree-based models, deep learning, and black-box systems, enabling developers to compute feature attributions and generate diagnostic visualizations like beeswarm and waterfall plots. Use this skill to build trust in your models, debug unexpected behaviors, analyze fairness, and implement production-ready explainable AI (XAI) workflows across any major framework including XGBoost, TensorFlow, and PyTorch.

Key Features

Specialized workflows for debugging, feature engineering, and fairness analysis
Optimization strategies for large datasets including sampling and batching techniques
0 GitHub stars
Rich visualization library including waterfall, beeswarm, bar, scatter, and force plots
Support for major ML frameworks: XGBoost, LightGBM, TensorFlow, PyTorch, and Scikit-learn
Automated explainer selection for tree, deep learning, linear, and black-box models

Use Cases

Improving model accuracy by identifying non-linear relationships and high-impact feature interactions
Detecting model bias and data leakage by analyzing feature interactions and attributions
Explaining individual model predictions to stakeholders for regulatory compliance or transparency

About

Key Features

Specialized workflows for debugging, feature engineering, and fairness analysis
Optimization strategies for large datasets including sampling and batching techniques
0 GitHub stars
Rich visualization library including waterfall, beeswarm, bar, scatter, and force plots
Support for major ML frameworks: XGBoost, LightGBM, TensorFlow, PyTorch, and Scikit-learn
Automated explainer selection for tree, deep learning, linear, and black-box models

Use Cases

Improving model accuracy by identifying non-linear relationships and high-impact feature interactions
Detecting model bias and data leakage by analyzing feature interactions and attributions
Explaining individual model predictions to stakeholders for regulatory compliance or transparency