shap-explainer

# shap-explainer ## Overview SHAP-based model explainability skill for feature attribution, summary plots, interaction analysis, and model interpretation. ## Capabilities - TreeExplainer for tree-based models (XGBoost, LightGBM, Random Forest) - DeepExplainer for neural networks - KernelExplainer for model-agnostic explanations - Summary, dependence, and force plots - Interaction value computation - Cohort-based analysis - Waterfall and bar plots - Expected value analysis ## Target Processes - Model Interpretability and Explainability Analysis - Model Evaluation and Validation Framework - A/B Testing Framework for ML Models ## Tools and Libraries - SHAP - matplotlib - numpy ## Input Schema ```json { "type": "object", "required": ["modelPath", "dataPath", "explainerType"], "properties": { "modelPath": { "type": "string", "description": "Path to the trained model" }, "dataPath": { "type": "string", "description": "Path to data for explanation" }, "explainerType": { "type": "string", "enum": ["tree", "deep", "kernel", "linear", "gradient"], "description": "Type of SHAP explainer to use" }, "analysisConfig": { "type": "object", "properties": { "numSamples": { "type": "integer" }, "backgroundSamples": { "type": "integer" }, "featureNames": { "type": "array", "items": { "type": "string" } }, "outputIndex": { "type": "integer" } } }, "plotCo...