train
Fit Predictive Models over Different Tuning Parameters
This function sets up a grid of tuning parameters for a number of classification and regression routines, fits each model and calculates a resampling based performance measure.
 Keywords
 models
Usage
train(x, ...)
"train"(x, y, method = "rf", preProcess = NULL, ..., weights = NULL, metric = ifelse(is.factor(y), "Accuracy", "RMSE"), maximize = ifelse(metric %in% c("RMSE", "logLoss"), FALSE, TRUE), trControl = trainControl(), tuneGrid = NULL, tuneLength = 3)
"train"(form, data, ..., weights, subset, na.action = na.fail, contrasts = NULL)
Arguments
 x
 an object where samples are in rows and features are in columns. This could be a simple matrix, data frame or other type (e.g. sparse matrix). See Details below.
 y
 a numeric or factor vector containing the outcome for each sample.
 method
 a string specifying which classification or regression model
to use. Possible values are found using
names(getModelInfo())
. See http://topepo.github.io/caret/bytag.html. A list of functions can also be passed for a custom model function. See http://topepo.github.io/caret/custom_models.html for details.  preProcess
 a string vector that defines a preprocessing of the
predictor data. Current possibilities are "BoxCox", "YeoJohnson",
"expoTrans", "center", "scale", "range", "knnImpute", "bagImpute",
"medianImpute", "pca", "ica" and "spatialSign". The default is no
preprocessing. See
preProcess
andtrainControl
on the procedures and how to adjust them. Preprocessing code is only designed to work whenx
is a simple matrix or data frame.  weights
 a numeric vector of case weights. This argument will only affect models that allow case weights.
 metric
 a string that specifies what summary metric will be used to
select the optimal model. By default, possible values are "RMSE" and
"Rsquared" for regression and "Accuracy" and "Kappa" for classification. If
custom performance metrics are used (via the
summaryFunction
argument intrainControl
, the value ofmetric
should match one of the arguments. If it does not, a warning is issued and the first metric given by thesummaryFunction
is used. (NOTE: If given, this argument must be named.)  maximize
 a logical: should the metric be maximized or minimized?
 trControl
 a list of values that define how this function acts. See
trainControl
and http://topepo.github.io/caret/training.html#custom. (NOTE: If given, this argument must be named.)  tuneGrid
 a data frame with possible tuning values. The columns are
named the same as the tuning parameters. Use
getModelInfo
to get a list of tuning parameters for each model or see http://topepo.github.io/caret/modelList.html. (NOTE: If given, this argument must be named.)  tuneLength
 an integer denoting the amount of granularity in the
tuning parameter grid. By default, this argument is the number of levels for
each tuning parameters that should be generated by
train
. IftrainControl
has the optionsearch = "random"
, this is the maximum number of tuning parameter combinations that will be generated by the random search. (NOTE: If given, this argument must be named.)  form
 A formula of the form
y ~ x1 + x2 + ...
 data
 Data frame from which variables specified in
formula
are preferentially to be taken.  subset
 An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)
 na.action
 A function to specify the action to be taken if NAs are
found. The default action is for the procedure to fail. An alternative is
na.omit
, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.)  contrasts
 a list of contrasts to be used for some or all the factors appearing as variables in the model formula.
 ...
 arguments passed to the classification or regression routine
(such as
randomForest
). Errors will occur if values for tuning parameters are passed here.
Details
train
can be used to tune models by picking the complexity parameters
that are associated with the optimal resampling statistics. For particular
model, a grid of parameters (if any) is created and the model is trained on
slightly different data for each candidate combination of tuning parameters.
Across each data set, the performance of heldout samples is calculated and
the mean and standard deviation is summarized for each combination. The
combination with the optimal resampling statistic is chosen as the final
model and the entire training set is used to fit a final model.
The predictors in x
can be most any object as long as the underlying
model fit function can deal with the object class. The function was designed
to work with simple matrices and data frame inputs, so some functionality
may not work (e.g. preprocessing). When using string kernels, the vector of
character strings should be converted to a matrix with a single column.
More details on this function can be found at
http://topepo.github.io/caret/training.html.
A variety of models are currently available and are enumerated by tag (i.e.
their model characteristics) at
http://topepo.github.io/caret/bytag.html.
Value

A list is returned of class
train
containing: containing:References
http://topepo.github.io/caret/training.html Kuhn (2008), ``Building Predictive Models in R Using the caret'' (http://www.jstatsoft.org/article/view/v028i05/v28i05.pdf)
See Also
models
, trainControl
,
update.train
, modelLookup
,
createFolds
Examples
library(caret)
## Not run:
#
# #######################################
# ## Classification Example
#
# data(iris)
# TrainData < iris[,1:4]
# TrainClasses < iris[,5]
#
# knnFit1 < train(TrainData, TrainClasses,
# method = "knn",
# preProcess = c("center", "scale"),
# tuneLength = 10,
# trControl = trainControl(method = "cv"))
#
# knnFit2 < train(TrainData, TrainClasses,
# method = "knn",
# preProcess = c("center", "scale"),
# tuneLength = 10,
# trControl = trainControl(method = "boot"))
#
#
# library(MASS)
# nnetFit < train(TrainData, TrainClasses,
# method = "nnet",
# preProcess = "range",
# tuneLength = 2,
# trace = FALSE,
# maxit = 100)
#
# #######################################
# ## Regression Example
#
# library(mlbench)
# data(BostonHousing)
#
# lmFit < train(medv ~ . + rm:lstat,
# data = BostonHousing,
# method = "lm")
#
# library(rpart)
# rpartFit < train(medv ~ .,
# data = BostonHousing,
# method = "rpart",
# tuneLength = 9)
#
# #######################################
# ## Example with a custom metric
#
# madSummary < function (data,
# lev = NULL,
# model = NULL) {
# out < mad(data$obs  data$pred,
# na.rm = TRUE)
# names(out) < "MAD"
# out
# }
#
# robustControl < trainControl(summaryFunction = madSummary)
# marsGrid < expand.grid(degree = 1, nprune = (1:10) * 2)
#
# earthFit < train(medv ~ .,
# data = BostonHousing,
# method = "earth",
# tuneGrid = marsGrid,
# metric = "MAD",
# maximize = FALSE,
# trControl = robustControl)
#
# #######################################
# ## Parallel Processing Example via multicore package
#
# ## library(doMC)
# ## registerDoMC(2)
#
# ## NOTE: don't run models form RWeka when using
# ### multicore. The session will crash.
#
# ## The code for train() does not change:
# set.seed(1)
# usingMC < train(medv ~ .,
# data = BostonHousing,
# method = "glmboost")
#
# ## or use:
# ## library(doMPI) or
# ## library(doParallel) or
# ## library(doSMP) and so on
#
# ## End(Not run)