The parameters accepted by the algorithm include:
-
objective
to define the type of task, say regression or classification; -
colsample_bytree
the subsample ratio of columns when constructing each tree. Subsampling happens once in every iteration. This number is usually a value between 0 and 1; -
learning_rate
that determines how fast or slow the model will learn; -
max_depth
indicates the maximum depth for each tree. The more the trees, the greater model complexity, and the higher chances of overfitting; -
alpha
is the L1 regularization on weights; -
n_estimators
is the number of decision trees to fit.