# 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, ...)`## 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
- a data frame containing training data where samples are in rows and features are in columns.
- 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 argument must
- 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:
`ada`

,`bag`

,`bagEarth`

,`bagFDA`

,`blackboost`

,`Boruta`

,`cforest`

,`ctree`

- ...
- 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
`center`

,`scale`

,`spatialSign`

,`pca`

,`ica`

, and`knnImpute`

. See - 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`

. (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 in each
method preceded by a period (e.g. .decay, .lambda). Also, a function can be passed to
`tuneGrid`

with arguments called`len<`

- tuneLength
- an integer denoting the number of levels for each tuning parameters that should be
generated by
`createGrid`

. (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.

A variety of models are currently available. The table below enumerates the models and the values of the `method`

argument, as well as the complexity parameters used by `train`

.

**Model** `method`

Value**Package** **Tuning Parameter(s)**
Generalized linear model `glm`

`glmStepAIC`

`gam`

`select`

, `method`

`gamLoess`

`span`

, `degree`

`gamSpline`

`df`

Recursive partitioning `rpart`

`maxdepth`

`ctree`

`mincriterion`

`ctree2`

`maxdepth`

Boosted trees `gbm`

`interaction depth`

,
`n.trees`

, `shrinkage`

`blackboost`

`maxdepth`

, `mstop`

`ada`

`maxdepth`

, `iter`

, `nu`

Boosted regression models `glmboost`

`mstop`

`gamboost`

`mstop`

`logitBoost`

`nIter`

Random forests `rf`

`mtry`

`parRF`

`mtry`

`cforest`

`mtry`

`Boruta`

`mtry`

Bagging `treebag`

`bag`

`vars`

`logicBag`

`ntrees`

, `nleaves`

Other Trees `nodeHarvest`

`maxinter`

, `node`

`partDSA`

`cut.off.growth`

, `MPD`

Logic Regression `logreg`

`ntrees`

, `treesize`

Elastic net (glm) `glmnet`

`alpha`

, `lambda`

Neural networks `nnet`

`decay`

, `size`

`neuralnet`

`layer1`

, `layer2`

, `layer3`

`pcaNNet`

`decay`

, `size`

Projection pursuit regression `ppr`

`nterms`

Principal component regression `pcr`

`ncomp`

Independent component regression `icr`

`n.comp`

Partial least squares `pls`

`ncomp`

Sparse partial least squares `spls`

`K`

, `eta`

, `kappa`

Support vector machines `svmLinear`

`C`

`svmRadial`

`sigma`

, `C`

`svmPoly`

`scale`

, `degree`

, `C`

Relevance vector machines `rvmLinear`

`rvmRadial`

`sigma`

`rvmPoly`

`scale`

, `degree`

Least squares support vector machines `lssvmRadial`

`sigma`

Gaussian processes `guassprLinearl`

`guassprRadial`

`sigma`

`guassprPoly`

`scale`

, `degree`

Linear least squares `lm`

`lmStepAIC`

`rlm`

`earth`

`degree`

, `nprune`

`gcvEarth`

`degree`

Bagged MARS `bagEarth`

`degree`

, `nprune`

Rule Based Regression `M5Rules`

`pruned`

Penalized linear models `penalized`

`lambda1`

, `lambda2`

`enet`

`lambda`

, `fraction`

`lars`

`fraction`

`lars2`

`steps`

`enet`

`fraction`

`foba`

`lambda`

, `k`

Supervised principal components `superpc`

`n.components`

, `threshold`

Quantile regression forests `qrf`

`mtry`

Quantile regression neural networks `qrnn`

`n.hidden`

, `penalty`

, `bag`

Linear discriminant analysis `lda`

`Linda`

`qda`

`QdaCov`

`slda`

`hda`

`newdim`

, `lambda`

, `gamma`

Stepwise discriminant analysis `stepLDA`

`maxvar`

, `direction`

`stepQDA`

`maxvar`

, `direction`

Stepwise diagonal discriminant analysis `sddaLDA`

`sddaQDA`

`sda`

`diagonal`

Sparse linear discriminant analysis `sparseLDA`

`NumVars`

, `lambda`

Regularized discriminant analysis `rda`

`lambda`

, `gamma`

Mixture discriminant analysis `mda`

`subclasses`

Sparse mixture discriminant analysis `smda`

`NumVars`

, `R`

, `lambda`

Penalized discriminant analysis `pda`

`lambda`

`pda2`

`df`

Stabilised linear discriminant analysis `slda`

`hdda`

`model`

, `threshold`

Flexible discriminant analysis (MARS) `fda`

`degree`

, `nprune`

Bagged FDA `bagFDA`

`degree`

, `nprune`

Logistic/multinomial regression `multinom`

`decay`

Penalized logistic regression `plr`

`lambda`

, `cp`

Rule--based classification `J48`

`C`

`OneR`

`PART`

`threshold`

, `pruned`

`JRip`

`NumOpt`

Logic Forests `logforest`

`vbmpRadial`

`estimateTheta`

k nearest neighbors `knn3`

`k`

Nearest shrunken centroids `pam`

`threshold`

`scrda`

`alpha`

, `delta`

Naive Bayes `nb`

`usekernel`

Generalized partial least squares `gpls`

`K.prov`

Learned vector quantization `lvq`

`size`

, `k`

ROC Curves `rocc`

`rocc`

`xgenes`

}

By default, the function `createGrid`

is used to define the candidate values of the tuning parameters. The user can also specify their own. To do this, a data fame is created with columns for each tuning parameter in the model. The column names must be the same as those listed in the table above with a leading dot. For example, `ncomp`

would have the column heading `.ncomp`

. This data frame can then be passed to `createGrid`

.

In some cases, models may require control arguments. These can be passed via the three dots argument. Note that some models can specify tuning parameters in the control objects. If specified, these values will be superseded by those given in the `createGrid`

argument.

The vignette entitled "caret Manual -- Model Building" has more details and examples related to this function.

`train`

can be used with "explicit parallelism", where different resamples (e.g. cross-validation group) can be split up and run on multiple machines or processors. By default, `train`

will use a single processor on the host machine. To use more, the `computeFunction`

and `computeArgs`

arguments in `trainControl`

can be used. `computeFunction`

is used to pass a function that takes arguments named `X`

and `FUN`

. Internally, `train`

will pass the data and modeling functions through using these arguments. By default, `train`

uses `lapply`

. Alternatively, any function that emulates `lapply`

but distributes jobs across multiple machines/processors can be used. Arguments to such a function can be passed (if needed) via the `computeArgs`

argument in `trainControl`

. Examples are given below using the

##### Value

- A list is returned of class
`train`

containing: modelType an identifier of the model type. results a data frame the training error rate and values of the tuning parameters. call the (matched) function call with dots expanded dots a list containing any ... values passed to the original call metric a string that specifies what summary metric will be used to select the optimal model. trControl the list of control parameters. finalModel an fit object using the best parameters trainingData a data frame resample A 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.perfNames a character vector of performance metrics that are produced by the summary function maximize a logical recycled from the function arguments.

##### References

Kuhn (2008), ``Building Predictive Models in R Using the caret'' (

##### See Also

##### 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 = c("center", "scale"),
tuneLength = 2,
trace = FALSE,
maxit = 100)
#######################################
## Regression Example
library(mlbench)
data(BostonHousing)
lmFit <- train(medv ~ . + rm:lstat,
data = BostonHousing,
"lm")
library(rpart)
rpartFit <- train(medv ~ .,
data = BostonHousing,
"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,
"earth",
tuneGrid = marsGrid,
metric = "MAD",
maximize = FALSE,
trControl = robustControl)
#######################################
## Parallel Processing Example via MPI
## A function to emulate lapply in parallel
mpiCalcs <- function(X, FUN, ...)
{
theDots <- list(...)
parLapply(theDots$cl, X, FUN)
}
library(snow)
cl <- makeCluster(5, "MPI")
## 50 bootstrap models distributed across 5 workers
mpiControl <- trainControl(workers = 5,
number = 50,
computeFunction = mpiCalcs,
computeArgs = list(cl = cl))
set.seed(1)
usingMPI <- train(medv ~ .,
data = BostonHousing,
"glmboost",
trControl = mpiControl)
################################################
## Parallel Random Forest using foreach and doMPI
library(doMPI)
cl <- startMPIcluster(count = 5, verbose = TRUE)
registerDoMPI(cl)
rfMPI <- train(medv ~ .,
data = BostonHousing,
"parRF")
closeCluster(cl)
#######################################
## Parallel Processing Example via NWS
nwsCalcs <- function(X, FUN, ...)
{
theDots <- list(...)
eachElem(theDots$sObj,
fun = FUN,
elementArgs = list(X))
}
library(nws)
sObj <- sleigh(workerCount = 5)
nwsControl <- trainControl(workers = 5,
number = 50,
computeFunction = nwsCalcs,
computeArgs = list(sObj = sObj))
set.seed(1)
usingNWS <- train(medv ~ .,
data = BostonHousing,
"glmboost",
trControl = nwsControl)
close(sObj)
#######################################
## Parallel Random Forest Models using
## the foreach package and MPI
library(doMPI)
cl <- startMPIcluster(2)
registerDoMPI(cl)
set.seed(1)
parallelRF <- train(medv ~ .,
data = BostonHousing,
"parRF")
closeCluster(cl)
```

*Documentation reproduced from package caret, version 4.75, License: GPL-2*