Gradient Boosting



Gradient boosting is an ensemble learning method in which a set of weak prediction models are iteratively made stronger by aggregating them to produce a stronger base model, which is then used to make a new set of even stronger models, and so on. Gradient boosting has been shown to work very well on many practical data mining tasks.


The simple prediction models are weak on their own but when combined they form a powerful base model. Gradient boosting also improves the predictive performance as new samples become available.


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