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I use boosting tree to make prediction for the stock direction, and it is a binary class classification.

The majority class is the down direction, and the minority class is the up direction. The boosting tree can make a better prediction for the majority class compared with minority class, or you can regard that the tree somewhat overfits to the majority class.

So I leave the test set as the same, while impose a bigger weight for the training loss calculation in the minority class of the training set. Finally, I observe that the whole loss of the test set can increase only a bit or decrease a lot, which depends on the weight imposed in the training loss. BUT, the worst thing is that the accuray of the majority class always decreases.

My question is that how can I improve the accuracy for both the majority class and minority class when using imbalance learning techs (some other imbalance learning methods can also be considered), Is it possible?

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Don't use accuracy as a KPI. Why is accuracy not the best measure for assessing classification models? Instead, use probabilistic class membership predictions and assess these using proper scoring rules.

What you are doing is related to over-/undersampling. It's better to use proper scoring rules. Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?

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