![]() With your set of data partitioned, you can pass a character vector with the pre-processing steps you want done. Pre-ProcessingĬaret provides a handful of standardized pre-processing steps which automatically ignore factor / non-numeric variables. The difference between createDataPartition and strata (from the sampling library) function is that you can also use numeric values as the stratification. Train_log <- createDataPartition(data$y,times = 1,ĬreateDataPartition allows you to create stratified samples based on a single variable. Data Splittingĭata <- read.table("~/in/bank/bank.csv",header=T, We’ll be working with the bank marketing data set from the UCI machine learning repository. Evaluate your model(s) on a holdout set.Are there particular parameter values you want to check?.Determine your parameter tuning strategy: Cross-validation? Bootstrapping?.Set up your pre-processing steps: centering / scaling? PCA? Imputing missing values?. ![]() Split your data into a training and testing set (perhaps using createDataPartition).Here’s how a caret training sessions breaks down: ![]() Trying multiple models and using the same steps has never been easier. The best part of the caret package is its uniformity.ĭespite there being (at the time of writing) 8,489 packages available on CRAN, the authors of caret have taken the time to incorporate over 210 models into the parameter tuning capabilities of the package.Īs a result, it’s very likely that your favorite R model can be used inside the caret package and can be automatically tuned for you. Users of this package are likely going to get more done as they spend less time tweaking their models manually. The more I use caret, the more I like it. Max Kuhn, the principal author of the package, goes around the country teaching courses in R and using this tool to aide model development. The oldest archive on CRAN is from October 2007 so it has been around for a while. Using a training and holdout sample, the caret package trains a model you provide and returns the optimal model based on an optimization metric. The caret package lets you quickly automate model tuning. Summary: The caret package was developed by Max Kuhn and contains a handful of great functions that help with parameter tuning. This entry was posted in Code in R on Septemby Will
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