shap: a game theoretic framework for explaining the predictions of any machine learning model

What it solves

SHAP (SHapley Additive exPlanations) solves the "black box" problem of machine learning by providing a game theoretic approach to explain the output of any ML model. It allows users to understand why a model made a specific prediction by allocating credit to each input feature based on its contribution to the final result.

How it works

SHAP uses Shapley values from game theory to assign an importance value to each feature for a specific prediction. It offers several specialized algorithms depending on the model type:

• TreeExplainer: A high-speed exact algorithm for tree ensembles like XGBoost, LightGBM, CatBoost, scikit-learn, and pyspark.
• DeepExplainer: A high-speed approximation for deep learning models (TensorFlow/Keras/PyTorch) based on the DeepLIFT algorithm.
• GradientExplainer: Approximates SHAP values using expected gradients, combining ideas from Integrated Gradients and SmoothGrad.
• KernelExplainer: A model-agnostic method that uses weighted local linear regression to estimate SHAP values for any function.
• LinearExplainer: Analytically computes exact SHAP values for linear models.

Who it’s for

Data scientists and ML engineers who need to interpret their models' predictions, debug model behavior, or provide transparency for stakeholders in fields like healthcare (e.g., survival models) or finance.

Highlights

• Universal Compatibility: Works with any machine learning model, including tree ensembles, deep learning networks, and linear models.
• Diverse Visualizations: Includes waterfall plots, force plots, beeswarm plots, and scatter plots to visualize feature impact.
• NLP Support: Specialized support for Hugging Face transformers to explain natural language predictions with few evaluations.
• Interaction Values: Ability to compute pairwise interaction effects to reveal hidden relationships between features.