How do I use SHAP for model interpretation?

What is SHAP and how can I use it for my machine learning model?

SHAP stands for SHapley Additive exPlanations. It's a method to explain the output of machine learning models by assigning each feature an importance value.

How do I install the SHAP library in Python?

You can install the SHAP library using pip. Just run pip install shap in your terminal.

Can you show me a simple example of using SHAP with a decision tree?

Sure! First, train your decision tree model, then use SHAP's TreeExplainer to get the SHAP values. Here's a code snippet: explainer = shap.TreeExplainer(model) and shap_values = explainer.shap_values(X).

What are some common visualizations I can create with SHAP?

You can create summary plots, dependence plots, and force plots to visualize SHAP values and feature importance.