Machine Learning

Gradient Boosting

An ensemble method that builds models sequentially to correct earlier errors.

Gradient boosting creates a sequence of weak models, usually shallow decision trees. Each new model focuses on reducing the errors that remain after the earlier models have made their predictions.

The final prediction combines the contribution of all learners. Gradient boosting can achieve excellent results on structured data, but its performance depends on careful control of tree depth, learning rate and the number of estimators.

Also in Machine Learning

Supervised Learning Unsupervised Learning K-Means K-Nearest Neighbours Logistic Regression Linear Regression

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