ml-model-explainer

Explain ML model predictions using SHAP values, feature importance, and decision paths with visualizations.

$ Instalar

git clone https://github.com/dkyazzentwatwa/chatgpt-skills /tmp/chatgpt-skills && cp -r /tmp/chatgpt-skills/ml-model-explainer ~/.claude/skills/chatgpt-skills

// tip: Run this command in your terminal to install the skill

SKILL.md

View on GitHub →

name: ml-model-explainer description: Explain ML model predictions using SHAP values, feature importance, and decision paths with visualizations.

ML Model Explainer

Explain machine learning model predictions using SHAP and feature importance.

Features

SHAP Values: Explain individual predictions
Feature Importance: Global feature rankings
Decision Paths: Trace prediction logic
Visualizations: Waterfall, force plots, summary plots
Multiple Models: Support for tree-based, linear, neural networks
Batch Explanations: Explain multiple predictions

Quick Start

from ml_model_explainer import MLModelExplainer

explainer = MLModelExplainer()
explainer.load_model(model, X_train)

# Explain single prediction
explanation = explainer.explain(X_test[0])
explainer.plot_waterfall('explanation.png')

# Feature importance
importance = explainer.feature_importance()

CLI Usage

python ml_model_explainer.py --model model.pkl --data test.csv --output explanations/

Dependencies

shap>=0.42.0
scikit-learn>=1.3.0
pandas>=2.0.0
numpy>=1.24.0
matplotlib>=3.7.0