train

0th

Percentile

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, ...)
"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 = 3)
"train"(form, data, ..., weights, subset, na.action = na.fail, 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.
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 function. See http://topepo.github.io/caret/custom_models.html for details.
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 preProcess and trainControl on the procedures and how to adjust them. Pre-processing code is only designed to work when x is a simple matrix or data frame.
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 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 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/caret/modelList.html. (NOTE: If given, this argument must be named.)
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 train. If trainControl has the option search = "random", this is the maximum number of tuning parameter combinations that will be generated by the random search. (NOTE: If given, this argument must be named.)
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 the factors appearing as variables in the model formula.
...
arguments passed to the classification or regression routine (such as randomForest). Errors will occur if values for tuning parameters are passed here.
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: containing:

References

http://topepo.github.io/caret/training.html Kuhn (2008), ``Building Predictive Models in R Using the caret'' (http://www.jstatsoft.org/article/view/v028i05/v28i05.pdf)

See Also

models, trainControl, update.train, modelLookup, createFolds

Aliases
  • train
  • train.default
  • train.formula
Examples
library(caret) ## 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)
Documentation reproduced from package caret, version 6.0-73, License: GPL (>= 2)

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