Preprocessing Data in R for ML with "caret" (2021)
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In this video I provide a beginning to a multi-part tutorial series on machine learning in R using the "caret" package. We will begin with pre-processing of a dataset to get it into a format appropriate for the machine learning pipeline, as well as identifying zero or near zero variance predictors. The beauty of this package is that it is truly a one stop shop for all of your machine learning needs.
There are a few sources from which this tutorial draws influence and structure. The first is the GitHub documentation on "caret" from its creation, Max Kuhn. The second is a very well-written and comprehensive tutorial by author Selva Prabhakaran on Machine Learning Plus. Third is a helpful resource for dealing with class imbalance, as we often find with classification problems.
GitHub documentation from Max Kuhn: https://topepo.github.io/caret/
Tutorial by Selva Prabhakaran: https://www.machinelearningplus.com/m...
Tutorial on "caret" with class imbalances: https://shiring.github.io/machine_lea...