Estimators support arguments to control the fitting behaviour -- these arguments are often called hyperparameters. Among the most important ones for GBRT are:

number of regression trees ( n_estimators )

) depth of each individual tree ( max_depth )

) loss function ( loss )

For example if you want to fit a regression model with 100 trees of depth 3 using least-squares: