Evaluating ML Performance, Resampling, and Workflows in "tidymodels" | R Tutorial (2021)
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Caret tutorial series:
Part 1: • Preprocessing Data in R for ML with "...
Part 2: • Feature Elimination and Variable Impo...
Part 3: • Training and Tuning ML Models in R wi...
Part 4: • Creating ROC curves and ensembling mo...
Tidymodels:
Part 1: • Intro to machine learning in R with "...
In the previous tutorial series, we walked through the "caret" package in R for machine learning. We used the raw "GermanCredit" dataset, performed a brief exploration of it, and used the package to walk through a variety of steps: pre-processing, removing low information features, tuning hyperparameters, correcting for class imbalance, and summarizing results based on metrics we deem important. Where possible, we will perform the exact same exercise here, except we will use the "tidymodels" suite of packages to do so.
There are a few sources from which this tutorial draws influence and structure.
- "Tutorial on tidymodels for Machine Learning": https://hansjoerg.me/2020/02/09/tidym...
- "Tidymodels: tidy machine learning in R": http://www.rebeccabarter.com/blog/202...
- "Caret vs. tidymodels - comparing the old and new" by Konrad Semsch: https://konradsemsch.netlify.app/2019...
- "Tidy Modeling with R" by Max Kuhn and Julia Silge: https://www.tmwr.org/
- Recursive feature elimination example by Max Kuhn: https://github.com/stevenpawley/recip...
- Documentation for "stacks": https://stacks.tidymodels.org/article...
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