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': {'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]]
eval_labels = [0, 1, 1, 0]
eval_dataset = Pool(eval_data,
eval_labels)
model = CatBoostClassifier(learning_rate=0.03,
custom_metric=['Logloss',
'AUC:hints=skip_train~false'])
model.fit(train_data,
train_labels,
eval_set=eval_dataset,
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.