ml-model-explainer

To explain machine learning model predictions using SHAP values, this Skill breaks down complex decisions into understandable components by calculating feature importance and generating visualizations for individual and batch predictions.

Visualizing feature importance and decision paths for tree-based models is fully supported. The Skill traces the logic of tree-based, linear, and neural network models to show exactly how a specific prediction was reached.

Interpreting why a machine learning model made a specific prediction involves using SHAP values to identify which features contributed most to a single outcome, making complex decisions transparent for stakeholders.

This SHAP explainer works with scikit-learn models and pandas dataframes by requiring shap, scikit-learn, pandas, numpy, and matplotlib to perform comprehensive analysis and generate visual outputs.

The best way to calculate global feature importance across all predictions is to analyze which features are generally most influential across the entire dataset, a core feature provided by this Skill's comprehensive analysis.

To save SHAP explanations and visualizations for your test dataset, you use the Skill to process a model file and test data, outputting the generated explanations and plots directly to a specified directory.