shap and kernelshap
KernelSHAP is a specialized implementation of one specific SHAP algorithm (kernel-based feature attribution), while SHAP is the comprehensive library containing multiple Shapley value methods; they are complements where the focused tool serves practitioners who need that particular algorithm variant alongside or instead of the general framework.
About shap
shap/shap
A game theoretic approach to explain the output of any machine learning model.
This tool helps data scientists and machine learning engineers understand why their machine learning models make specific predictions. By taking a trained model and input data, it shows how much each individual feature contributes to the final output, clarifying complex model behavior. It's designed for anyone building or using ML models who needs to explain their results, like a business analyst evaluating a credit risk model or a medical researcher interpreting a diagnostic tool.
About kernelshap
ModelOriented/kernelshap
Different SHAP algorithms
When working with complex models in R, understanding why a model makes a specific prediction can be challenging. This tool helps practitioners interpret model predictions by calculating SHAP values, which show how much each input feature contributes to the final output. It takes a trained model and a dataset as input and outputs numerical SHAP values that can then be visualized to explain individual predictions or overall model behavior. This is useful for data scientists, statisticians, or analysts who build and deploy predictive models and need to explain their reasoning to stakeholders.
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