Which visualization should I use for global feature importance?

For global understanding, use the beeswarm plot to see feature importance distributions or the bar plot for a clean summary of feature rankings across the entire dataset.

Can SHAP help with model fairness and bias?

Yes, by computing SHAP values for protected attributes or demographic groups, you can identify if your model is relying on biased proxies or treating specific cohorts unfairly.

How do I explain a single prediction using SHAP?

To explain an individual output, use a waterfall plot or force plot. These visualizations show how each feature value contributed to shifting the prediction from the baseline value.

What model types does this SHAP skill support?

It supports a wide range of models including tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and generic black-box models.

SHAP Model Interpretability

Name: SHAP Model Interpretability
Author: pur3v4d3r

bypur3v4d3r

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データサイエンスとML

Simplifies machine learning model interpretation by generating SHAP values and visualizations for model transparency and debugging.

概要

SHAP (SHapley Additive exPlanations) is a comprehensive skill designed to make complex machine learning models transparent and explainable. It provides guidance on choosing the right explainer for various model types—including tree-based, deep learning, and linear models—and facilitates the creation of insightful visualizations like beeswarm, waterfall, and scatter plots. By leveraging cooperative game theory, this skill helps developers identify feature importance, detect model bias, and debug unexpected predictions, ultimately fostering trust and accountability in AI-driven decision-making.

主な機能

Unified model explanation across tree-based, deep learning, and black-box models
1 GitHub stars
Comprehensive visualization suite including waterfall, beeswarm, and force plots
Automated selection of optimal SHAP explainers for specific model architectures
Advanced workflows for model debugging, bias detection, and fairness analysis
Performance optimization strategies for large-scale datasets and production environments

ユースケース

Identifying and mitigating feature bias or data leakage during model development
Explaining specific model predictions to stakeholders for transparency and regulatory compliance
Comparing multiple models to select the most interpretable and consistent solution

概要

主な機能

Unified model explanation across tree-based, deep learning, and black-box models
1 GitHub stars
Comprehensive visualization suite including waterfall, beeswarm, and force plots
Automated selection of optimal SHAP explainers for specific model architectures
Advanced workflows for model debugging, bias detection, and fairness analysis
Performance optimization strategies for large-scale datasets and production environments

ユースケース

Identifying and mitigating feature bias or data leakage during model development
Explaining specific model predictions to stakeholders for transparency and regulatory compliance
Comparing multiple models to select the most interpretable and consistent solution