in the most recent playground competition on kaggle (https://www.kaggle.com/c/tabular-playground-series-mar-2021/overview/evaluation) we once again have an evaluation via the area under the roc curve. When we use the .predict_proba methods (i.e. output probabilities instead of hard values 0, 1) we always get a significantly higher score.
But I do not quite understand how this is works for the ROC AUC metric. It makes sense for log loss as we actually calculate a difference, but does the True positive rate change? I think it shouldn't. Or is the metric actually doing some subtraction under that hood?
Thanks for any pointers!