get_best_score

Return the best result for each metric calculated on each validation dataset.

Method call format

get_best_score()

Type of return value

dict

Output format:
{pool_name_1: {metric_1: value,..., metric_N: value}, ..., pool_name_M: {metric_1: value,..., metric_N: value}
For example:
{'validation_0': {'Logloss': 0.6085537606941837, 'AUC': 0.0}}

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_score()
Note. This example illustrates the usage of the method with the CatBoostClassifier class. The usage with other classes is identical.