Explain Machine Learning Model using SHAP

Learn SHAP tool to understand feature contribution in prediction model.

Most of the Machine Learning and Neural Network models are difficult to interpret. Generally, Those models are a BlackBox that makes it hard to understand, explain, and interpret. Data scientists always focus only on output performance of a model but not on model interpretabiility and explainability. Data Scientists need certain tools to understand and explain the model for an intuitive understanding of the machine learning model. We have one such tool SHAP that explain how Your Machine Learning Model Works. SHAP(SHapley Additive exPlanations) provides the very useful for model explainability using simple plots such as summary and force plots.

In this article, we’re going to explain model explainability using SHAP package in python.

Source: https://shap.readthedocs.io/en/latest/index.html

What is SHAP?

SHAP stands for SHapley Additive exPlanations. It is based on a game theoretic approach and explains the output of any machine learning model using visualization tools.

SHAP Characteristics

• It is mainly used for explaining the predictions of any…