Machine Learning with R, the tidyverse, and mlr by Hefin I. Rhys

Author:Hefin I. Rhys
Language: eng
Format: mobi, epub
Publisher: Manning Publications
Published: 2020-03-31T05:39:52.029000+00:00

Note

Recall that I mentioned how important it is to scale our predictors so that they are weighted equally when calculating the L1 and/or L2 norms. Well, glmnet does this for us by default, using its standardize = TRUE argument. This is handy, but it’s important to remember that the parameter estimates are transformed back onto the variables’ original scale.

Listing 11.8. Extracting the model parameters

ridgeModelData <- getLearnerModel(tunedRidgeModel)

ridgeCoefs <- coef(ridgeModelData, s = tunedRidgePars$x$s)

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