Description Usage Format Details Examples

`caret_pre_model`

provides a model setup for the train function of
package caret

1 |

An object of class `list`

of length 17.

When tuning parameters of `pre()`

with caret's `train()`

function, always use the default S3 method (i.e., specify predictors and response
variables through arguments `x`

and `y`

. When `train.formula()`

,
is used (i.e., when `formula`

and `data`

arguments are specified),
`train`

will internally call `model.matrix()`

on `data`

, which
will code all categorical (factor) predictor variables as dummy variables, and
will yield a different result than inputting the original factors, for most
tree-based methods.

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## Not run:
library("caret")
## Prepare data:
airq <- airquality[complete.cases(airquality),]
y <- airq$Ozone
x <- airq[,-1]
## Apply caret with only pre's default settings (trControl and ntrees argument
## are employed here only to reduce computation time):
set.seed(42)
prefit1 <- train(x = x, y = y, method = caret_pre_model,
trControl = trainControl(number = 1),
ntrees = 25L)
prefit1
## Create custom tuneGrid:
set.seed(42)
tuneGrid <- caret_pre_model$grid(x = x, y = y,
maxdepth = 3L:5L,
learnrate = c(.01, .1),
penalty.par.val = c("lambda.1se", "lambda.min"))
tuneGrid
## Apply caret (again, ntrees and trControl set only to reduce computation time):
prefit2 <- train(x = x, y = y, method = caret_pre_model,
trControl = trainControl(number = 1),
tuneGrid = tuneGrid, ntrees = 25L)
prefit2
## Get best tuning parameter values:
prefit2$bestTune
## Get predictions from model with best tuning parameters:
predict(prefit2, newdata = x[1:10, ])
plot(prefit2)
## Obtain tuning grid through random search over the tuning parameter space:
set.seed(42)
tuneGrid2 <- caret_pre_model$grid(x = x, y = y, search = "random", len = 10)
tuneGrid2
set.seed(42)
prefit3 <- train(x = x, y = y, method = caret_pre_model,
trControl = trainControl(number = 1, verboseIter = TRUE),
tuneGrid = tuneGrid2, ntrees = 25L)
prefit3
## Count response:
set.seed(42)
prefit4 <- train(x = x, y = y, method = caret_pre_model,
trControl = trainControl(number = 1),
ntrees = 25L, family = "poisson")
prefit4
## Binary factor response:
y_bin <- factor(airq$Ozone > mean(airq$Ozone))
set.seed(42)
prefit5 <- train(x = x, y = y_bin, method = caret_pre_model,
trControl = trainControl(number = 1),
ntrees = 25L, family = "binomial")
prefit5
## Factor response with > 2 levels:
x_multin <- airq[,-5]
y_multin <- factor(airq$Month)
set.seed(42)
prefit6 <- train(x = x_multin, y = y_multin, method = caret_pre_model,
trControl = trainControl(number = 1),
ntrees = 25L, family = "multinomial")
prefit6
## End(Not run)
``` |

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