Classification vs regression machine learning?

Question

While studying data mining methods I have come to understand that there are two main categories:

-Predictive methods:

• classification
• Regression

-Descriptive methods:

• Clustering
• 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".

First question is: can classification provide me with the probability/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

Second question is, 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)

Answer 1

Answer1: Some classification methods give you the probability of each class. The Naive Bayes and logistic regression are two of these methods. Note that logistic regression is a classification method, not a regression method!

Answer2: Your problem is inherently a classification problems, not regression. As mentioned in the Answer1, you can use classification methods which give you the probabilities. If you want to use regression methods, you need to provide training samples where the targets are continuous, that is you should provide each training sample with your confidence about her/his availability.