Feature Elimination and Variable Importance in R with "caret" (2021)
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In this video I continue with the multi-part tutorial series that began in the last video with the "caret" package. We will finish the process of applying transformations to the training set, and applying the same specified transformations to the testing set. We'll then get into algorithmically removing low information features via recursive feature elimination, training our first model, and then creating variable importance plots where applicable.
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...
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