How can I use SHAP in Python for model interpretation?

How can I use SHAP in Python for model interpretation?

You can use the SHAP library in Python to explain the output of machine learning models. First, install it using pip: pip install shap. Then, you can create SHAP values for your model's predictions.

Can you show me a simple example?

Sure! Here's a basic example using a decision tree model:

import shap
import xgboost as xgb

# Load data and train model
X, y = shap.datasets.boston()
model = xgb.XGBRegressor().fit(X, y)

# Create SHAP explainer and calculate SHAP values
explainer = shap.Explainer(model)
shap_values = explainer(X)

# Visualize the SHAP values
shap.summary_plot(shap_values, X)

What do the SHAP values represent?

SHAP values represent the contribution of each feature to the model's prediction for a specific instance. Positive SHAP values indicate a feature that pushes the prediction higher, while negative values indicate a feature that lowers the prediction.