To illustrate a partial dependence plot with a categorical feature.a.very complexb.average for each categorical valuec.globald.both local and global

The answer is b. average for each categorical value.

A partial dependence plot (PDP) shows the marginal effect one or two features have on the predicted outcome of a machine learning model. For a categorical feature, the PDP is calculated as the average prediction of the model for each category of the feature.

Here are the steps to illustrate a partial dependence plot with a categorical feature:

1. Fit your machine learning model with your dataset.
2. Select the categorical feature for which you want to create the PDP.
3. For each category in the selected feature, replace all instances of that category in the dataset with the average prediction of the model for that category.
4. Plot the average predictions for each category on the y-axis and the categories on the x-axis. This will give you a visual representation of how the model's predictions change with different categories of the feature.