SHAP (SHapley Additive exPlanations)
SHAP (SHapley Additive exPlanations) is a game theory-based approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions.
Overview
SHAP values interpret the impact of having a certain value for a given feature in comparison to the prediction we’d make if that feature took some baseline value. In other words, how much each feature in the dataset contributed to the prediction. It provides a measure of importance for each feature and how they contribute to the prediction for each individual instance.
How SHAP Works
SHAP assigns each feature an importance value for a particular prediction. Its goal is to explain the prediction of an instance x by computing the contribution of each feature to the prediction. The SHAP explanation method computes Shapley values from coalitional game theory. The feature values of a data instance act as players in a coalition. Shapley values tell us how to fairly distribute the “payout” (the prediction) among the features.
Benefits of SHAP
- Consistency: If a model changes such that it relies more on a feature, the attributed importance for that feature should not decrease.
- Local Accuracy: The sum of the feature contributions and the expected model prediction for the baseline should equal the original prediction.
- Missingness: Features that are not present in a model do not contribute to the prediction.
- Global Interpretability: The sum of SHAP values for a feature over a dataset provides a measure of the global importance of that feature.
Applications of SHAP
SHAP is widely used in various fields where interpretability of machine learning models is crucial. It’s used in healthcare for interpreting complex models predicting disease risks, in finance for credit scoring, and in many other domains where understanding the decision-making process of a model is important.
Limitations of SHAP
While SHAP provides a robust way to interpret machine learning models, it’s not without its limitations. The computation of SHAP values can be quite intensive, especially for complex models and large datasets. This can make it less practical for real-time applications.
SHAP in Python
The SHAP library in Python provides a powerful and easy-to-use toolset for computing and visualizing SHAP values. It supports a wide range of models and is designed to be “model-agnostic”, meaning it can be used with any machine learning model.
import shap
# load JS visualization code to notebook
shap.initjs()
# train XGBoost model
X,y = shap.datasets.boston()
model = xgboost.train({"learning_rate": 0.01}, xgboost.DMatrix(X, label=y), 100)
# explain the model's predictions using SHAP
explainer = shap.Explainer(model)
shap_values = explainer(X)
# visualize the first prediction's explanation
shap.plots.waterfall(shap_values[0])
In conclusion, SHAP provides a powerful and flexible way to interpret machine learning models. It combines a solid theoretical foundation with practical applications, making it a valuable tool for data scientists.
