shap/shap

What it solves

SHAP (SHapley Additive exPlanations) solves the "black box" problem in machine learning by providing a way to explain the output of any model. It allows users to understand exactly which features contributed to a specific prediction, moving from a simple prediction to a transparent explanation of why that prediction was made.

How it works

SHAP uses a game theoretic approach based on Shapley values to allocate "credit" among input features. It calculates how much each feature pushes the model output away from the average base value. Depending on the model type, it uses different algorithms:

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

Who it’s for

Data scientists and ML engineers who need to interpret their models' predictions for debugging, auditing, or communicating results to stakeholders.

Highlights

• Universal Compatibility: Works with any machine learning model, including tree-based models, deep neural networks, and natural language processing models via Hugging Face transformers.
• Diverse Visualizations: Includes waterfall plots, force plots, beeswarm plots, and scatter plots to visualize feature importance and interactions.
• GPU Acceleration: Supports CUDA for faster Tree SHAP computations.
• Interaction Values: Can compute pairwise interaction effects to reveal hidden relationships between features.