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train(x, ...)## S3 method for class 'default':
train(x, y,
method = "rf",
...,
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)
y ~ x1 + x2 + ...formula are preferentially to be taken.ada, bagEarth, bagFDA, blackboost, cforest, ctree, ctree2, earthrandomForest). Errors will occur if values
for tuning parameters are passed here.trainControl. (NOTE: If given, this argument must be named.)createGrid in this createGrid. (NOTE: If given, this argument must be named.)train containing:NULL. The returnResamp argument of trainControl
controls how much of the resampled results are saved.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.
method Value Package Tuning Parameter(s)
Generalized linear model glm 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
Bagged trees treebag nodeHarvest maxinter, node
Elastic net (glm) glmnet alpha, lambda
Neural networks nnet decay, size
pcaNNet decay, size
Projection pursuit regression ppr nterms
Principal component regression pcr ncomp
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 rlm earth degree, nprune
Bagged MARS bagEarth degree, nprune
Rule Based Regression M5Rules pruned
Elastic net enet lambda, fraction
Least Angle Regression lars fraction
lars2 steps
The Lasso enet fraction
Penalized linear models penalized lambda1, lambda2
Supervised principal components superpc n.components, threshold
Linear discriminant analysis lda Linda qda QdaCov slda 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 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
Bayesian multinomial probit model vbmpRadial estimateTheta
k nearest neighbors knn3 k
Nearest shrunken centroids pam threshold
Naive Bayes nb usekernel
Generalized partial least squares gpls K.prov
Learned vector quantization lvq k
}
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
trainControl, createGrid, createFolds#######################################
## Classification Example
data(iris)
TrainData <- iris[,1:4]
TrainClasses <- iris[,5]
knnFit1 <- train(TrainData, TrainClasses,
"knn",
tuneLength = 10,
trControl = trainControl(method = "cv"))
knnFit2 <- train(TrainData, TrainClasses,
"knn", tuneLength = 10,
trControl = trainControl(method = "boot"))
library(MASS)
nnetFit <- train(TrainData, TrainClasses,
"nnet",
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)Run the code above in your browser using DataLab