I want to predict the probability of rain based on the measured weather parameters like temperature, humidity, etc. Let's not get into why I want to do that despite the fact that weather websites already publish the probability of rain.
I want to implement this using logistic regression. I have weather data for 2012, every 15 minutes for each day. I also know the date and time during which it rained (Yes/No label). Feature vector comprises a concatenation of weather parameters (temperature, atmospheric pressure, wind speed, humidity) for t0, t0-15 min and t0-30m minutes if it rained at t0. Thus, I can create my supervised learning dataset with positive examples.
However, I am confused about how many negative examples I should choose? Negative feature vector would be derived in a similar fashion but when t0 does not have rain.
Here are my questions:
Should I choose equal number of positive and negative examples? Does my learning depend on how many examples of each category I include in my training set?
If learning does depend upon the number of positive and negative examples, how many negative examples should I choose?
I know there are many other ways to doing this prediction but please try to answer the questions regarding logistic regression only. I am not looking for other approaches.