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", 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 = ifelse(trControl$method == "none", 1, 3)) ## S3 method for class 'formula' train(form, data, ..., weights, subset, na.action = na.fail, contrasts = NULL)
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) but must have column names. See Details below. Preprocessing using the |
... |
arguments passed to the classification or regression routine
(such as |
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 |
preProcess |
a string vector that defines a 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
pre-processing. See |
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 |
maximize |
a logical: should the metric be maximized or minimized? |
trControl |
a list of values that define how this function acts. See
|
tuneGrid |
a data frame with possible tuning values. The columns are
named the same as the tuning parameters. Use |
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 |
form |
A formula of the form |
data |
Data frame from which variables specified in |
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
|
contrasts |
a list of contrasts to be used for some or all the factors appearing as variables in the model formula. |
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/model-training-and-tuning.html.
A variety of models are currently available and are enumerated by tag (i.e. their model characteristics) at http://topepo.github.io/caret/train-models-by-tag.html.
A list is returned of class train
containing:
method
|
the chosen model. |
modelType |
an identifier of the model type. |
results |
a data frame the training error rate and values of the tuning parameters. |
bestTune |
a data frame with the final 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. |
control |
the list of control parameters. |
preProcess
|
either |
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 |
perfNames |
a character vector of performance metrics that are produced by the summary function |
maximize |
a logical recycled from the function arguments. |
yLimits |
the range of the training set outcomes. |
times |
a list of execution times: |
Max Kuhn (the guts of train.formula
were based on Ripley's
nnet.formula
)
http://topepo.github.io/caret/
Kuhn (2008), “Building Predictive Models in R Using the caret” (http://www.jstatsoft.org/article/view/v028i05/v28i05.pdf)
models
, trainControl
,
update.train
, modelLookup
,
createFolds
## 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)