Can this skill help with model debugging?

Yes, by analyzing the SHAP values of misclassified samples, you can pinpoint which feature attributions led the model to make an incorrect prediction.

Does this skill provide visualization code?

Absolutely. The skill includes implementation patterns for all major SHAP plots, including waterfall, beeswarm, scatter, bar, and heatmaps.

What is the difference between global and local importance in SHAP?

Global importance provides an overview of how features affect the model across the entire dataset (e.g., beeswarm plots), while local importance explains the specific feature contributions for a single prediction (e.g., waterfall plots).

What models does this SHAP skill support?

It supports a wide range of models including tree-based frameworks like XGBoost and LightGBM, deep learning models in TensorFlow or PyTorch, linear models, and any black-box model via the universal KernelExplainer.

SHAP Model Explainability

Name: SHAP Model Explainability
Author: eyadsibai

byeyadsibai

데이터 과학 및 ML

Explains machine learning model predictions using Shapley values to provide interpretable feature importance and attribution.

소개

The SHAP (SHapley Additive exPlanations) skill empowers Claude to interpret, debug, and validate machine learning models through mathematically grounded feature attribution. It provides standardized patterns for implementing various explainers—including Tree, Deep, and Kernel—allowing developers to visualize global model behavior and individual prediction breakdowns. This skill is essential for data scientists looking to move beyond black-box models and provide transparent, actionable insights into how specific features influence model outcomes.

주요 기능

Supports specialized explainers for Tree-based, Deep Learning, and Linear models
Identifies model bias and aids in debugging misclassified samples
Generates global importance summaries using beeswarm and bar plots
Analyzes feature interactions and relationships with scatter plots
Provides local prediction explanations through waterfall and force visualizations
0 GitHub stars

사용 사례

Explaining individual model predictions to non-technical stakeholders
Debugging model errors by identifying which features caused a specific failure
Auditing model fairness by analyzing the influence of sensitive features

소개

주요 기능

Supports specialized explainers for Tree-based, Deep Learning, and Linear models
Identifies model bias and aids in debugging misclassified samples
Generates global importance summaries using beeswarm and bar plots
Analyzes feature interactions and relationships with scatter plots
Provides local prediction explanations through waterfall and force visualizations
0 GitHub stars

사용 사례

Explaining individual model predictions to non-technical stakeholders
Debugging model errors by identifying which features caused a specific failure
Auditing model fairness by analyzing the influence of sensitive features