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I saw on a CatBoost site that it supposed to outperform any other boosted training model and decided to try it myself on a Kaggle's https://www.kaggle.com/c/house-prices-advanced-regression-techniques.

I created some basic kernel without any complex preprocessing, feature selection, GridSearch, stacking, etc... just to compare the XGBR and CatBoost performance. But as far as I see the XGBR always outperforms the CatBoost.

https://www.kaggle.com/markbquant/compare-catboostregressor-vs-xgbregressor

**The CatBoost parameters**: iterations=100, depth=3, learning_rate=0.1

**The XGBR parameters**: subsample=0.7, colsample_bytree=0.7, n_estimators=500, learning_rate=0.03, max_depth=5, min_child_weight=3

For example, inside the Kaggle, the XGBR received score 0.134 and the CatBoot only reached 0.197 (I tried both one_hot_max_size and cat_features). I'll be thankful if someone could point me what is wrong with the CatBoost model, may be some optimization is missing.

So on how many different datasets / tasks did you do your comparison experiment? 1? – Isbister – 2018-11-29T18:59:48.870