Gradient Boosting Explained
Kirill dives into the intricacies of gradient boosting, explaining how it builds models sequentially to predict the gradients of the loss function. He highlights the relationship between the loss function and residuals, revealing that the derivative of the loss function is essentially the residual itself. This elegant connection underscores the deliberate choice of the mean squared error as the loss function in regression problems.In this clip
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