After reasearching I realized that scale_pos_weight is typically calculated as the ratio of the number of negative samples to the number of positive samples in the training data. My dataset has 840 negative samples and 2650 positive samples so the ratio is 0.32. If my samples were other way round I sure believe that scale_pos_weight would be a better approach.Is it save to assume that since it is less 1 then it will still balance correctly? Of course specificity is important in my study but our goal is more about recall, precision and f1 score. Can this contribute to more false positives by impacting the specificity the most?