get_best_iteration

Return the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set.

Method call format

get_best_iteration()

Type of return value

int or None if the validation dataset is not specified.

Usage examples

from catboost import CatBoostClassifier, Pool

train_data = [[0,3],
              [4,1],
              [8,1],
              [9,1]]

train_labels = [0,0,1,1]

eval_data = [[2,1],
            [3,1],
            [9,0],
            [5,3]]

evalution_pool = Pool(eval_data)

model = CatBoostClassifier(learning_rate = 0.03, custom_metric = ['Logloss', 'AUC:hints=skip_train~false'])

model.fit(train_data, train_labels, eval_set=evalution_pool, verbose=False)

print model.get_best_iteration()