Let's learn the basics of the package here.

Let's learn how to explain a binary classifier with the Adult dataset.

Let's learn how to explain a multi-class classifier with the Iris dataset.

Let's learn how to explain a regressor with the Boston dataset.

Let's learn how to explain an image classifier with the MNIST dataset.

Let's learn how to reach targets for multiple features at the same time to capture correlations.

Let's explain a classifier that predicts the presence of heart disease.

Let's explain a classifier that predicts the presence of diabetes.

Let's compare to PDP, which basically consists in showing what the model predicts on average when each data instance has the value v for that feature, while ignoring whether the value v makes sense for all data instances.