I have a dataset with around 900.000 records, around 1000 of which are marked as positive (the studied event occurred).
The probability of the event occurring is always low (i.e. < 0.1), and I would like to create a regression model to predict the probability of the event occurring.
My first thought was to use logistic regression, but I am not sure if I could directly interpret the output as the probability of the event happening. The same doubt arises when using other models, such as SVM or RF.
Another doubt would be whether usual evaluation metrics (e.g. RMSE) would work well on such a model, since even a predictor that always outputs 0 would have a very good score.