All You Need to Know about Gradient Boosting Algorithm − Part 2. Classification

Algorithm explained with an example, math, and code

In the Part 1 article, we learned the gradient boosting regression algorithm in its detail. As we reviewed in that post, the algorithm is flexible enough to deal with any loss functions as long as it is differentiable. That means if we just replace the loss function used for regression, specifically mean squared loss, with a loss function that deals with classification problems, we can perform classification without …

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