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I trained a regression model with lightgbm and the learning curve doesn't look good: enter image description here

The model variance increases during training, which shows a kind of overfitting. Now, I tried many ways to fix this to no avail: regularization, features subsampling, max depth reduction, etc. Of course, the train and test datasets have been derived from the same initial dataset by a random split so they are both representative.

In your opinion, what could explain this behavior, and how could it be fixed?

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