# train

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##### 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, ...)## S3 method for class 'default':
train(x, y,
method = "rf",
preProcess = NULL,
...,
weights = NULL,
metric = ifelse(is.factor(y), "Accuracy", "RMSE"),
maximize = ifelse(metric == "RMSE", FALSE, TRUE),
trControl = trainControl(),
tuneGrid = NULL,
tuneLength = 3)## S3 method for class 'formula':
train(form, data, ..., weights, subset, na.action, 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.
form
A formula of the form y ~ x1 + x2 + ...
data
Data frame from which variables specified in formula are preferentially to be taken.
weights
a numeric vector of case weights. This argument will only affect models that allow case weights.
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 a
contrasts
a list of contrasts to be used for some or all of the factors appearing as variables in the model formula.
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 fun
...
arguments passed to the classification or regression routine (such as randomForest). Errors will occur if values for tuning parameters are passed here.
preProcess
a string vector that defines an pre-processing of the predictor data. Current possibilities are "BoxCox", "YeoJohnson", "expoTrans", "center", "scale", "range", "knnImpute", "bagImpute", "medianImpute", "pca", "ica" and "spatialSign". The default is no pr
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
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
tuneLength
an integer denoting the number of levels for each tuning parameters that should be generated by train. (NOTE: If given, this argument must be named.)
##### 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 held-out 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. pre-processing). 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:
• methodthe chosen model.
• modelTypean identifier of the model type.
• resultsa data frame the training error rate and values of the tuning parameters.
• bestTunea data frame with the final parameters.
• callthe (matched) function call with dots expanded
• dotsa list containing any ... values passed to the original call
• metrica string that specifies what summary metric will be used to select the optimal model.
• controlthe list of control parameters.
• preProcesseither NULL or an object of class preProcess
• finalModelan fit object using the best parameters
• trainingDataa data frame
• resampleA data frame with columns for each performance metric. Each row corresponds to each resample. If leave-one-out cross-validation or out-of-bag estimation methods are requested, this will be NULL. The returnResamp argument of trainControl controls how much of the resampled results are saved.
• perfNamesa character vector of performance metrics that are produced by the summary function
• maximizea logical recycled from the function arguments.
• yLimitsthe range of the training set outcomes.
• timesa list of execution times: everything is for the entire call to train, final for the final model fit and, optionally, prediction for the time to predict new samples (see trainControl)

• \cpkg
• \bpkg

##### href

• http://CRAN.R-project.org/package=#1
• http://www.bioconductor.org/packages/release/bioc/html/#1.html

• #1
• #1

##### References

http://topepo.github.io/caret/training.html

Kuhn (2008), Building Predictive Models in R Using the caret'' (http://www.jstatsoft.org/v28/i05/)

models, trainControl, update.train, modelLookup, createFolds

##### Aliases
• train
• train.default
• train.formula
##### Examples
#######################################
## 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

lev = NULL,
model = NULL) {
out <- mad(data$obs - data$pred,
na.rm = TRUE)
out
}

marsGrid <- expand.grid(degree = 1, nprune = (1:10) * 2)

earthFit <- train(medv ~ .,
data = BostonHousing,
method = "earth",
tuneGrid = marsGrid,
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
Documentation reproduced from package caret, version 6.0-35, License: GPL-2

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