Performing a simple ML model with R
In this section, we are going to go over performing a simple ML model in R. There are so many different ways to do this in R that it would be impossible for me to list them all, however, CRAN has done this so you and I don’t have to. If you want to see a task view of ML on CRAN, you can follow this link: https://cran.r-project.org/view=MachineLearning.
For this section, we are going to use the XGBoost algorithm as implemented by the healthyR.ai package. The algorithm is not written differently, the only difference is how data is saved in the output. The healthyR.ai package also contains a preprocessor for the XGBoost algorithm to ensure that the input data matches what the algorithm is expecting before modeling. The two main functions that we will be using are hai_xgboost_data_prepper() and hai_auto_xgboost().
We will not cover loading the data in again as it was covered previously. Let’s get started!
