Glossary

Boosting: an approach to ensemble building where each subsequent model takes into account the errors of the previous one and, in the final prediction, the forecasts of basic learners.

Ensemble: a set of models for solving the same problem. The strength of ensembles is that the mean error of a group of models is less significant than their individual errors.

Practice

COPYpython
1# CatBoost classifier training
2# cat_features - list of categorical features
3from catboost import CatBoostClassifier
4
5model = CatBoostClassifier(loss_function="Logloss", iterations=50)
6model.fit(features_train, target_train, cat_features=cat_features, verbose=10)