I am trying to understand how probability works with a threshold for logistic regression.
I understand the basics of how to calculate probability.
log odds = intercept+value1*coef1 odds = exp(log odds) prob = odds / (1+odds)
I understand that a threshold is used to find the optimal mix of correct predictions (precision, f1, etc.).
However, how do we interpret a probability in light of a threshold? For example, if a threshold is
0.195, and a user has a probability of
0.0975 are they:
- 50% likely to respond (1) since they are 50% towards the threshold?
- Or are they still 0.0975% likely to respond (1), irrespective of how we consider the fact that anyone who is more then 0.195% likely is going to respond (1)?