Automatic model creation, for model selection, in polynomial regression in R

Question

Let's imagine that for a target value 'price', I have predictive variables of x, y, z, m, and n. I have been able to analyse different models that I could fit through following methods:

Forward, backward, and stepwise selection
Grid and Lasso
KNN (IBk)

For each I got RMSE and MSE for prediction and I can choose the best model. All these are helpful with linear models. I'm just wondering if there is any chance to do the same for polynomial regressions (squared, cubic, ...) so I can fit and analyse them as well in the same dataset.

Daniel Gimenez · Accepted Answer · 2017-11-23 16:10:26Z

0

Have you seen caret package? Its very powerfull and groups a lot of machine learning models. It can compares different models and also see the best metaparameters.

http://topepo.github.io/caret/index.html

answered Nov 23, 2017 at 16:10

Daniel Gimenez

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