= R caret =

'''caret''' ('''''C''lassification ''A''nd ''RE''gression ''T''raining''') is a library of functions for model training.

<<TableOfContents>>

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== Installation ==

{{{
install.packages('caret')
}}}

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== Usage ==

The '''`createDataPartition`''' function randomly partitions a vector of outcomes.

{{{
data(mtcars)

parts = createDataPartition(mtcars$mpg, p = .8)
train = mtcars[parts, ]
test = mtcars[-parts, ]
}}}

`createDataPartition` returns a list object. To instead return a matrix, pass `list = FALSE` as an option.

The '''`confusionMatrix`''' function prepares the confusion matrix for a prediction, such as that from an [[R/XGBoost|XGBoost]] model.

{{{
my.data <- xgb.DMatrix(data = df[,predictors], label=df$outcome)
my.model <- xgboost(data = my.data, objective = "binary:logistic", ...)
pred <- predict(my.model, my.data)
pred <- ifelse(pred > 0.5, 1, 0)

confusionMatrix(as.factor(pred), as.factor(df$outcome))
}}}



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