Fast Gradient Boosting with CatBoost

Fast Gradient Boosting with CatBoost

In this piece, we’ll take a closer look at a gradient boosting library called CatBoost.

In gradient boosting, predictions are made from an ensemble of weak learners. Unlike a random forest that creates a decision tree for each sample, in gradient boosting, trees are created one after the other. Previous trees in the model are not altered. Results from the previous tree are used to improve the next one. In this piece, we’ll take a closer look at a gradient boosting library called CatBoost.

CatBoost is a depth-wise gradient boosting library developed by Yandex. It uses oblivious decision trees to grow a balanced tree. The same features are used to make left and right splits for each level of the tree.

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As compared to classic trees, the oblivious trees are more efficient to implement on CPU and are simple to fit.

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