I have basic knowledge on machine learning techniques and I'm working on a real-world regression problem which is basically predicting the time consumption of a certain task based on a number of predictor values. Unfortunately (as usual) the underlying relationship is not known to me. However, I do assume that there are different types of tasks in my data and therefore I'm searching for a technique to handle this.
Breaking it down to a simpler regression problem: I have one predictor p1, a categorial predictor p2, and the resulting time t, and my data looks looked like this:
The regression becomes rather easy, as long as p2 is used by the regression model.
My Questions are:
- Is there any type of machine learning technique for regression that does this kind of "included classification"? What I means is that it automatically builds regression models for the seperate classes, if that improves the model.
- Would that technique work if the second predictor would not be categorial but "Type A" had p2 between 10 and 20, and "Type B" had p2 values between 30 and 40?
- What are good approaches to handling categorial predictors in regression?
- Any other advice for my described real-world problem?
Thanks for your help
//Edit 17.12.: Removed Example without p2