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Many kernels on kaggle use tree-based ensemble algorithms for supervised machine learning problems, such as adaboost, random forests, lightgbm, xgboost or catboost.
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Boosting is one of the most influential developments in categorization methodology.
Adaboost, short for adaptive boosting, is a machine acquisition meta-algorithm formulated aside yoav freund and robert schapire.
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