Gradient boosting is typically used with decision trees (especially CART regression trees) of a fixed size as base learners. For this special case Friedman proposes a modification to gradient boosting method which improves the quality of fit of each base learner.

from miml import datasets

fn = os.path.join(datasets.get_data_home(), 'regression',
'diabetes.csv')

>>> run script...