The SHAP

The SHAP (SHapley Additive exPlanations)

The SHAP (SHapley Additive exPlanations)

SHAP (Shapley Additive Explanations) is a method used in machine learning for explaining the output of any machine learning model. It assigns each feature an importance value for a particular prediction. The importance of each feature is determined by calculating the contribution of each feature to the prediction across all possible feature combinations. This method enables us to understand the contribution of each feature to the final prediction, making it easier to interpret and understand complex models.

The SHAP (SHapley Additive exPlanations) values are a way to explain the output of a machine learning model by assigning importance scores to each feature in the input data. The SHAP values represent the contribution of each feature to the difference between the expected output of the model and the actual output for a given input sample.

In this case, the curves represent the SHAP values for each feature ('Feature 1', 'Feature 2', and 'Feature 3') across all 100 input samples. The x-axis represents the sample index, and the y-axis represents the SHAP value.

A positive SHAP value for a feature indicates that the feature has a positive impact on the model's output for that sample, while a negative SHAP value indicates that the feature has a negative impact. The magnitude of the SHAP value represents the strength of the feature's impact, with larger magnitudes indicating stronger impacts.