catboost does not overfit - how is that possible? I'm fitting and evaluating a CatBoostRegressor and a XGBRegressor to the same regression problem. I tried matching their hyperparameters as closely as possible, yet I'm seeing something strange: catboost test error is monotonically decreasing! Why is that? NOTE that I switched off the fancy boosting_type and it's plain. 
catboost learning curve:

xgboost learning curve:

catboost get_all_params:
{'nan_mode': 'Min',
 'eval_metric': 'RMSE',
 'iterations': 1000,
 'sampling_frequency': 'PerTree',
 'fold_permutation_block': 0,
 'leaf_estimation_method': 'Newton',
 'boosting_type': 'Plain',
 'feature_border_type': 'GreedyLogSum',
 'bayesian_matrix_reg': 0.1000000015,
 'l2_leaf_reg': 3,
 'random_strength': 1,
 'rsm': 1,
 'boost_from_average': True,
 'model_size_reg': 0.5,
 'approx_on_full_history': False,
 'subsample': 0.8000000119,
 'use_best_model': True,
 'random_seed': 0,
 'depth': 6,
 'has_time': False,
 'fold_len_multiplier': 2,
 'border_count': 254,
 'classes_count': 0,
 'sparse_features_conflict_fraction': 0,
 'leaf_estimation_backtracking': 'AnyImprovement',
 'best_model_min_trees': 1,
 'model_shrink_rate': 0,
 'loss_function': 'RMSE',
 'learning_rate': 0.009999999776,
 'score_function': 'Cosine',
 'task_type': 'CPU',
 'leaf_estimation_iterations': 1,
 'bootstrap_type': 'MVS',
 'permutation_count': 4}


xgboost get_params:
{'base_score': 0.5,
 'booster': 'gbtree',
 'colsample_bylevel': 1,
 'colsample_bynode': 1,
 'colsample_bytree': 1,
 'gamma': 0,
 'importance_type': 'gain',
 'learning_rate': 0.01,
 'max_delta_step': 0,
 'max_depth': 6,
 'min_child_weight': 1,
 'missing': None,
 'n_estimators': 1000,
 'n_jobs': 1,
 'nthread': None,
 'objective': 'reg:linear',
 'random_state': 0,
 'reg_alpha': 0,
 'reg_lambda': 1,
 'scale_pos_weight': 1,
 'seed': None,
 'silent': False,
 'subsample': 1,
 'verbosity': 1}

 A: As it stands the two runs are not directly comparable. For example, off the bat, we can see that:


*

*catboost uses a stronger $L_2$ regularisation on the weights ($3$ instead of $1$ in xgboost).

*catboost uses different bagging/subsampling in terms of rows ($0.8$ instead of $1.0$ in xgboost).

*catboost uses a different way than xgboost to built its trees; symmetric instead of best-first in XGBoost.

*catboost uses an extra regularisation parameter (bayesian_matrix_reg) to regularise leaf values calculation (the non-diagonal ones when computing the hessian on the leafs); strictly speaking xgboost do not have the same functionality. 


Please note that the above points are not even a very in-depth look. While we might expect the same qualitative behaviour between different implementations of a general computational framework (e.g. lowering the depth of the base-learner trees decreases the chance of over-fitting), the effect size might not be comparable. As these implementations incorporate a number of computational techniques to accelerate and/or make them more stable, the direct comparison of their behaviour is unreliable. 
