I'm doing some machine learning work in python with sklearn. Basically the problem is trying to predict how long an event will take based on 4 correlated pieces of data. What I've been doing is I have four polynomial regression trained on each piece of data respectively. Then I take the output of these polynomial regressions and train a linear regression based on that. Testing this against historically data has show that the two step process has about 10% less sum-squared-error than just using the linear regression. Should I not be doing it this way? What is the theory behind just using a one step process vs. using both steps?
Training a model based on polynomial regression and then putting that into a linear regression, vs just using the linear regression
machine learning