I want to specify more than one evaluation metric for my CatBoostRegressor:
model=catboost.CatBoostRegressor(eval_metric=['RMSE', 'MAE', 'R2'])
So I can get the results very simple with the .get_best_score() method, but it does not accept the metrics in a list. Is there any way to do this? I could not figure it out nor find an answer. I know that it is easy to solve another way, but I want to know if this can be done with a different input format for the metrics or something or it is not supported. Thank you in advance!
You should pass the list of evaluation metrics to custom_metric instead of eval_metric:
from catboost import CatBoostRegressor
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
# generate the data
X, y = make_regression(n_samples=100, n_features=10, random_state=0)
# split the data
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=0)
# fit the model
model = CatBoostRegressor(iterations=10, custom_metric=['RMSE', 'MAE', 'R2'])
model.fit(X=X_train, y=y_train, eval_set=(X_valid, y_valid), silent=True)
# get the best score
print(model.get_best_score())
# {'learn': {
# 'MAE': 42.36387514896515,
# 'R2': 0.9398622316668792,
# 'RMSE': 54.878286259899525
# },
# 'validation': {
# 'MAE': 102.37559908734613,
# 'R2': 0.6989698975428136,
# 'RMSE': 134.75006267018009
# }}