I have a data set of attempted phone calls. I have a set of features, say, hour of day, and zip code. I have a label indicating whether the callee picked up the phone or not.
I want a model to predict the probability of a phone pick up given a instance's feature set
My difficulty is that I'm not interested in predicting whether the phone will be picked up or not, which would fit into a standard binary classification model, because I do not expect there to be very strong correlation between the features and the event. I'm merely hoping to discover that there is some boost in probability in a pick up for instance given its feature set. Then I could use that to prioritize phone numbers to attempt calling.
I don't think this fits neatly into a binary classifier model. What techniques/models can I look into for this problem.
Specifically, I'm looking for a model type to train with the data, that I can evaluate on a test set, and that will hopefully get better with more data.
I'm pretty new to this, as I'm sure you can tell, so any help would be greatly appreciated.