While studying data mining methods I have come to understand that there are two main categories:
- Association rules
Since I want to predict the user availability (output) based on location, activity, battery level (input for the training model), I think it's obvious that I would choose "Predictive methods", but now I can't seem to choose between classification and regression. From what I understand this far, classification can solve my problem, because the output is "available" or "not available".
Can classification provide me with the probability (or likelihood) of the user being available or not available?
As in the output wouldn't just be 0 (not available) or 1 (for available), but it's be something like:
- $80\%$ available
- $20\%$ not available
Can this problem also be solved using regression?
I get that regression is used for continuous output (not just 0 or 1 outputs), but can't the output be the continuous value of the user availability (like the output being $80$ meaning user is $80\%$ available, implicitly the user is $20\%$ unavailable).