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Caret tutorial series:
Part 1: [ Ссылка ]
Part 2: [ Ссылка ]
Part 3: [ Ссылка ]
Part 4: [ Ссылка ]
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": [ Ссылка ]
- "Tidymodels: tidy machine learning in R": [ Ссылка ]
- "Caret vs. tidymodels - comparing the old and new" by Konrad Semsch: [ Ссылка ]
- "Tidy Modeling with R" by Max Kuhn and Julia Silge: [ Ссылка ]
- Recursive feature elimination example by Max Kuhn: [ Ссылка ]
- Documentation for "stacks": [ Ссылка ]
