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The Caret Package
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November 30, 2021
An essential resource for using the brilliant caret (Classification And REgression Training) package for supervised machine learning modelling in R. I probably refer to it a couple of times a month for work and university.
It isn’t very beginner friendly, but great for diving head first into machine learning for regression and classification, preprocessing (centring and scaling, principal component analysis, boosted tree imputation, stratified data partitioning etc.), training models, setting train controls (cross validation and bootstrapping), hyperparameter tuning (tune grid and tune length), model evaluation (RMSE/R-squared, confusion matrices etc.), and variable importance for those already fluent in base R and the tidyverse suite of packages.
I’d recommend Hadley Wickham’s R for data science for getting a grasp of the basics of R and the tidyverse.
Both books are available to read online in html format for free.
It isn’t very beginner friendly, but great for diving head first into machine learning for regression and classification, preprocessing (centring and scaling, principal component analysis, boosted tree imputation, stratified data partitioning etc.), training models, setting train controls (cross validation and bootstrapping), hyperparameter tuning (tune grid and tune length), model evaluation (RMSE/R-squared, confusion matrices etc.), and variable importance for those already fluent in base R and the tidyverse suite of packages.
I’d recommend Hadley Wickham’s R for data science for getting a grasp of the basics of R and the tidyverse.
Both books are available to read online in html format for free.
Displaying 1 of 1 review