- Published on December 25, 2021
- In Deep Tech
Complete Guide to SHAP – SHAPley Additive exPlanations for Practitioners
SHAP or SHAPley Additive exPlanations is a visualization tool that can be used for explaining the prediction of any model by computing the contribution of each feature to the prediction
- By Yugesh Verma
There are many machine learning models which are very accurate and high performing while making predictions. One of the limitations with these models we always find is that we can not explain the quality of outcomes produced by them. There is always a need to make the outcomes from the model more explainable. In this article, we are going to introduce a tool “SHAP (SHAPley Additive exPlanations)” that can help us in making the outcomes of the machine learning models more explainable. The major points to be discussed in this article are listed below.
Table of contentsWhat is SHAP?How to Installing SHAP ?Simple Implementation of SHAPExplaining Models With SHAPely ValuesExamining the Model CoefficientsPartial Dependence PlotsComputing the SHAP valuesPartial dependence plotWaterfa
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Yugesh Verma
Yugesh is a graduate in automobile engineering and worked as a data analyst intern. He completed several Data Science projects. He has a strong interest in Deep Learning and writing blogs on data science and machine learning.
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