I'm training the logistic regression for binary classification on a labeled data set. Now I'm using the same entries and predict their scores using the model.
For example, I have an entry with label 0 and predicted score is 0.1 and another entry with known label 0 and predicted score 0.2. So basically I'm using model to get probability for seen (as opposite to unseen) data.
And I'm trying to argue whether the predicted probability shows the ordering/ranking of the entries - second entry from the example is closer to class 1 than first entry? Or do they just show the performance of my model?
This contradicts to common approach when trained model is used on unseen data, and I feel that comparing the scores of training data has no sense, but I can't understand why