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I am running repeated K-fold Cross-Validation on my dataset using different models. My problem is a regression problem and I am counting on the error metric MAE. I do know that some models may behave better that others, but I cannot explain the drastic change in the behavior of the learning curves in each: enter image description here

Specifically, I want to know what makes the learning curve for the gradient boost so bad. I assume its not learning anything. Is that normal ?

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It seems that boosting algo is overfitting because train error is around 0 constantly. You could try to reduce cimplexity of the model e.g. try to use smaller tree depth or some other stopping criteria.

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