Catboost now supports early_stopping_rounds: fit method parameters
Sets the overfitting detector type to Iter and stops the training
after the specified number of iterations since the iteration with the
optimal metric value.
This works very much like early_stopping_rounds in xgboost.
Here is an example:
from catboost import CatBoostRegressor, Pool
from sklearn.model_selection import train_test_split
import numpy as np
y = np.random.normal(0, 1, 1000)
X = np.random.normal(0, 1, (1000, 1))
X[:, 0] += y * 2
X_train, X_eval, y_train, y_eval = train_test_split(X, y, test_size=0.1)
train_pool = Pool(X, y)
eval_pool = Pool(X_eval, y_eval)
model = CatBoostRegressor(iterations=1000, learning_rate=0.1)
model.fit(X, y, eval_set=eval_pool, early_stopping_rounds=10)
The result should be something like this:
522: learn: 0.3994718 test: 0.4294720 best: 0.4292901 (514) total: 957ms remaining: 873ms
523: learn: 0.3994580 test: 0.4294614 best: 0.4292901 (514) total: 958ms remaining: 870ms
524: learn: 0.3994495 test: 0.4294806 best: 0.4292901 (514) total: 959ms remaining: 867ms
Stopped by overfitting detector (10 iterations wait)
bestTest = 0.4292900745
bestIteration = 514
Shrink model to first 515 iterations.
