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I am using xgboost with python in order to perform a binary classification in which the class 0 appears roughly 9 times more frequently than the class 1. I am of course using scale_pos_weight=9. However, when I perform the prediction on the testing data after training the model using train_test_split, I obtain a y_pred with twice the elements belonging to the class 1 than it should (20% instead of 10%). How can I correct this output? I thought the scale_pos_weight=9 would be enough to inform the model the expected proportion.

donut
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Your question seems sketchy: what is y_pred?

+Remember you are better to run a grid search or Bayesian optimizer to figure out the best scores.

Reza Paradise
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