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, ...)# S3 method for default
train(
x,
y,
method = "rf",
preProcess = NULL,
...,
weights = NULL,
metric = ifelse(is.factor(y), "Accuracy", "RMSE"),
maximize = ifelse(metric %in% c("RMSE", "logLoss", "MAE", "logLoss"), FALSE, TRUE),
trControl = trainControl(),
tuneGrid = NULL,
tuneLength = ifelse(trControl$method == "none", 1, 3)
)
# S3 method for formula
train(form, data, ..., weights, subset, na.action = na.fail, contrasts = NULL)
# S3 method for recipe
train(
x,
data,
method = "rf",
...,
metric = ifelse(is.factor(y_dat), "Accuracy", "RMSE"),
maximize = ifelse(metric %in% c("RMSE", "logLoss", "MAE"), FALSE, TRUE),
trControl = trainControl(),
tuneGrid = NULL,
tuneLength = ifelse(trControl$method == "none", 1, 3)
)
For the default method, x is 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 preProcess argument only supports matrices or data
frames. When using the recipe method, x should be an
unprepared recipe object that describes the model
terms (i.e. outcome, predictors, etc.) as well as any
pre-processing that should be done to the data. This is an
alternative approach to specifying the model. Note that, when
using the recipe method, any arguments passed to preProcess
will be ignored. See the links and example below for more details
using recipes.
Arguments passed to the classification or
regression routine (such as
randomForest). Errors will occur if
values for tuning parameters are passed here.
A numeric or factor vector containing the outcome for each sample.
A string specifying which classification or
regression model to use. Possible values are found using
names(getModelInfo()). See
http://topepo.github.io/caret/train-models-by-tag.html. A
list of functions can also be passed for a custom model
function. See
http://topepo.github.io/caret/using-your-own-model-in-train.html
for details.
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.
A numeric vector of case weights. This argument will only affect models that allow case weights.
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.)
A logical: should the metric be maximized or minimized?
A list of values that define how this function
acts. See trainControl and
http://topepo.github.io/caret/using-your-own-model-in-train.html.
(NOTE: If given, this argument must be named.)
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/available-models.html.
(NOTE: If given, this argument must be named.)
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.)
A formula of the form y ~ x1 + x2 + ...
Data frame from which variables specified in
formula or recipe are preferentially to be taken.
An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)
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.)
A list of contrasts to be used for some or all the factors appearing as variables in the model formula.
A list is returned of class train containing:
The chosen model.
An identifier of the model type.
A data frame the training error rate and values of the tuning parameters.
A data frame with the final parameters.
The (matched) function call with dots expanded
A list containing any ... values passed to the original call
A string that specifies what summary metric will be used to select the optimal model.
The list of control parameters.
Either NULL or an object of class
preProcess
A fit object using the best parameters
A data frame
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.
A character vector of performance metrics that are produced by the summary function
A logical recycled from the function arguments.
The range of the training set outcomes.
A list of execution times: everything is for
the entire call to train, final for the final
model fit and, optionally, prediction for the time to
predict new samples (see trainControl)
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.
More details on using recipes can be found at
http://topepo.github.io/caret/using-recipes-with-train.html.
Note that case weights can be passed into train using a
role of "case weight" for a single variable. Also, if
there are non-predictor columns that should be used when
determining the model's performance metrics, the role of
"performance var" can be used with multiple columns and
these will be made available during resampling to the
summaryFunction function.
http://topepo.github.io/caret/
Kuhn (2008), ``Building Predictive Models in R Using the caret'' (10.18637/jss.v028.i05)
models, trainControl,
update.train, modelLookup,
createFolds, recipe
# NOT RUN {
# }
# 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)
#######################################
## Example with a recipe
data(cox2)
cox2 <- cox2Descr
cox2$potency <- cox2IC50
library(recipes)
cox2_recipe <- recipe(potency ~ ., data = cox2) %>%
## Log the outcome
step_log(potency, base = 10) %>%
## Remove sparse and unbalanced predictors
step_nzv(all_predictors()) %>%
## Surface area predictors are highly correlated so
## conduct PCA just on these.
step_pca(contains("VSA"), prefix = "surf_area_",
threshold = .95) %>%
## Remove other highly correlated predictors
step_corr(all_predictors(), -starts_with("surf_area_"),
threshold = .90) %>%
## Center and scale all of the non-PCA predictors
step_center(all_predictors(), -starts_with("surf_area_")) %>%
step_scale(all_predictors(), -starts_with("surf_area_"))
set.seed(888)
cox2_lm <- train(cox2_recipe,
data = cox2,
method = "lm",
trControl = trainControl(method = "cv"))
#######################################
## 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
# }
# NOT RUN {
# }
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