train {caret} | R Documentation |
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.
train(x, ...) ## Default S3 method: train(x, y, method = "rf", ..., 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, ..., subset, na.action, contrasts = NULL)
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. |
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 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 , bagEarth , bagFDA , blackboost , cforest , ctree , ctree2 , earth , enet , fda , gamboost , gaussprPoly , gaussprRadial , gbm , glm , glmboost , glmnet , gpls , J48 , JRip , knn , lars , lasso , lda , lm , lmStepAIC , LMT , logitBoost , lssvmPoly , lssvmRadial , lvq , M5Rules , mda , multinom , nb , nnet , OneR , pam , pcaNNet , pda , pda2 , penalized , pls , ppr , qda , rda , rf , rpart , rvmPoly , rvmRadial , sda , sddaLDA , sddaQDA , slda , sparseLDA , spls , superpc , svmPoly , svmRadial and treebag . See the Details section below. |
... |
arguments passed to the classification or regression routine (such as randomForest ). Errors will occur if values
for tuning parameters are passed here. |
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 in trainControl , the value of metric should match one of the arguments. If it does not, a warning is issued and the first metric given by the summaryFunction 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 . (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). See the function createGrid in this package for more details.
(NOTE: If given, this argument must be named.) |
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.) |
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 | stats | none |
Recursive partitioning | rpart | rpart | maxdepth |
ctree | party | mincriterion |
|
ctree2 | party | maxdepth |
|
Boosted trees | gbm | gbm | interaction depth , |
n.trees , shrinkage |
|||
blackboost | mboost | maxdepth , mstop |
|
ada | ada | maxdepth , iter , nu |
|
Boosted regression models | glmboost | mboost | mstop |
gamboost | mboost | mstop |
|
logitboost | caTools | nIter |
|
Random forests | rf | randomForest | mtry |
cforest | party | mtry |
|
Bagged trees | treebag | ipred | None |
Elastic net (glm) | glmnet | glmnet | alpha , lambda |
Neural networks | nnet | nnet | decay , size |
Projection pursuit regression | ppr | stats | nterms |
Partial least squares | pls | pls, caret | ncomp |
Sparse partial least squares | spls | spls, caret | K , eta , kappa |
Support vector machines (RBF) | svmradial | kernlab | sigma , C |
Support vector machines (polynomial) | svmpoly | kernlab | scale , degree , C |
Relevance vector machines (RBF) | rvmradial | kernlab | sigma |
Relevance vector machines (polynomial) | rvmpoly | kernlab | scale , degree |
Least squares support vector machines (RBF) | lssvmradial | kernlab | sigma |
Gaussian processes (RBF) | guassprRadial | kernlab | sigma |
Gaussian processes (polynomial) | guassprPoly | kernlab | scale , degree |
Linear least squares | lm | stats | None |
Multivariate adaptive regression splines | earth | earth | degree , nprune |
Bagged MARS | bagEarth | caret, earth | degree , nprune |
M5 rules | M5Rules | RWeka | pruned |
Elastic net | enet | elasticnet | lambda , fraction |
Least Angle Regression | lars | lars | fraction |
lars2 | lars | steps |
|
The Lasso | enet | elasticnet | fraction |
Penalized linear models | penalized | penalized | lambda1 , lambda2 |
Supervised principal components | superpc | superpc | n.components , threshold |
Linear discriminant analysis | lda | MASS | None |
Quadratic discriminant analysis | qda | MASS | None |
Stabilised Linear discriminant analysis | slda | ipred | None |
Stepwise diagonal discriminant analysis | sddaLDA , sddaQDA | SDDA | None |
Shrinkage discriminant analysis | sda | sda | diagonal |
Regularized discriminant analysis | rda | klaR | lambda , gamma |
Mixture discriminant analysis | mda | mda | subclasses |
Penalized discriminant analysis | pda | mda | lambda |
pda2 | mda | df |
|
Stabilised linear discriminant analysis | slda | ipred | None |
Flexible discriminant analysis (MARS) | fda | mda, earth | degree , nprune |
Bagged FDA | bagFDA | caret, earth | degree , nprune |
Logistic/multinomial regression | multinom | nnet | decay |
C4.5 decision trees | J48 | RWeka | C |
Single Rule | OneR | RWeka | None |
PART | PART | RWeka | threshold , pruned |
k nearest neighbors | knn3 | caret | k |
Nearest shrunken centroids | pam | pamr | threshold |
Naive Bayes | nb | klaR | usekernel |
Generalized partial least squares | gpls | gpls | K.prov |
Learned vector quantization | lvq | class | 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 Rmpi package (via snow) and NetworkSpaces (via the nws package).
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. |
Max Kuhn (the guts of train.formula
were based on Ripley's nnet.formula
)
Kuhn (2008), ``Building Predictive Models in R Using the caret'' (http://www.jstatsoft.org/v28/i05/)
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 ## Not run: ## A function to emulate lapply in parallel mpiClacs <- function(X, FUN, ...) { theDots <- list(...) parLapply(theDots$cl, X, FUN) } library(snow) cl <- makeCluster(5, "MPI") ## 50 bootstrap models distributed across 5 workers mpiControl <- trainControl(workers = 5, number = 50, computeFunction = mpiClacs, computeArgs = list(cl = cl)) set.seed(1) usingMPI <- train(medv ~ ., data = BostonHousing, "glmboost", trControl = mpiControl) stopCluster(cl) ## End(Not run) ####################################### ## Parallel Processing Example via NWS ## Not run: nwsClacs <- function(X, FUN, ...) { theDots <- list(...) eachElem(theDots$sObj, fun = FUN, elementArgs = list(X)) } library(nws) sObj <- sleigh(workerCount = 5) nwsControl <- trainControl(workers = 5, number = 50, computeFunction = nwsClacs, computeArgs = list(sObj = sObj)) set.seed(1) usingNWS <- train(medv ~ ., data = BostonHousing, "glmboost", trControl = nwsControl) close(sObj) ## End(Not run)