How a Logistic Regression model reacts if the probability of success and failure are exactly 0.5?
To simply put, the model is not confident in making the decision. You can choose either of the class labels based on your problem -- do you want high precision or high recall ? However a more systematic way is to use ROC curve to find the optimal threshold value.
We know that the default threshold for success in a Binary Classification of the Logistic Regression model is > 0.5.
I'm curious to know the output of this model if the predicted probability is exactly 0.5 for both Success and Failure. Can someone clarify me?