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Subscribe to RichardOnData here: / @richardondata Patreon: / richardondata GitHub: https://github.com/RichardOnData/YouT... Part 1: • Preprocessing Data in R for ML with "caret... Part 2: • Feature Elimination and Variable Importanc... Part 3: • Training and Tuning ML Models in R with "c... In this video I continue the tutorial series on machine learning in R to talk about the following topics: modifying boundaries for classifier thresholds, creating ROC curves, and ensembling the results of multiple models. We will supplement the "caret" package with "ROCR" and "caretEnsemble". 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...