Im new to regression, but quit experienced in classification and machine learning. In Classification, the state of the art technique for classification is SVM, so the first solution I would like to use for my regression problem is an SVM one.
The regression problem im trying to solve can be described as follows:
I have one dependent variable which i would like to estimate with 100,1000,4000 independent variables respectively, and estimate the accuracy with each size. I have around 100,000 observations which Im planning to use in a 90%-10% cross validation scheme.
The generating process is probably not linear, this is why I want to use SVM regression (I will test linear kernel, though)
Question is, before I even attempt such process, what is the expected run-time for one regression iteration with linear or RBF kernel? Can anybody give a raw estimation? Is this problem feasible in the regression world?