Description Usage Arguments Details Value Author(s) See Also Examples
This function generates a control object that can be used to specify the details of the feature selection algorithms used in this package.
1 2 3 4 5 6 7 8 9 10 11 12 13 | rfeControl(functions = NULL,
rerank = FALSE,
method = "boot",
saveDetails = FALSE,
number = ifelse(method %in% c("cv", "repeatedcv"), 10, 25),
repeats = ifelse(method %in% c("cv", "repeatedcv"), 1, number),
verbose = FALSE,
returnResamp = "final",
p = .75,
index = NULL,
timingSamps = 0,
seeds = NA,
allowParallel = TRUE)
|
functions |
a list of functions for model fitting, prediction and variable importance (see Details below) |
rerank |
a logical: should variable importance be re-calculated each time features are removed? |
method |
The external resampling method: |
number |
Either the number of folds or number of resampling iterations |
repeats |
For repeated k-fold cross-validation only: the number of complete sets of folds to compute |
saveDetails |
a logical to save the predictions and variable importances from the selection process |
verbose |
a logical to print a log for each external resampling iteration |
returnResamp |
A character string indicating how much of the resampled summary metrics should be saved. Values can be “final”, “all” or “none” |
p |
For leave-group out cross-validation: the training percentage |
index |
a list with elements for each external resampling iteration. Each list element is the sample rows used for training at that iteration. |
timingSamps |
the number of training set samples that will be used to measure the time for predicting samples (zero indicates that the prediction time should not be estimated). |
seeds |
an optional set of integers that will be used to set the seed at each resampling iteration. This is useful when the models are run in parallel. A value of |
allowParallel |
if a parallel backend is loaded and available, should the function use it? |
More details on this function can be found at http://caret.r-forge.r-project.org/featureselection.html.
Backwards selection requires function to be specified for some operations.
The fit
function builds the model based on the current data set. The arguments for the function must be:
x
the current training set of predictor data with
the appropriate subset of variables
y
the current outcome data (either a numeric or
factor vector)
first
a single logical value for whether the
current predictor set has all possible variables
last
similar to first
, but TRUE
when the last model is fit with the final subset size and
predictors.
...
optional arguments to pass to the fit
function in the call to rfe
The function should return a model object that can be used to generate predictions.
The pred
function returns a vector of predictions (numeric or factors) from the current model. The arguments are:
object
the model generated by the fit
function
x
the current set of predictor set for the
held-back samples
The rank
function is used to return the predictors in the order of the most important to the least important. Inputs are:
object
the model generated by the fit
function
x
the current set of predictor set for the
training samples
y
the current training outcomes
The function should return a data frame with a column called var
that has the current variable names. The first row should be the most important predictor etc. Other columns can be included in the output and will be returned in the final rfe
object.
The selectSize
function determines the optimal number of predictors based on the resampling output. Inputs for the function are:
x
a matrix with columns for the performance
metrics and the number of variables, called
"Variables
"
metric
a character string of the performance
measure to optimize (e.g. "RMSE", "Rsquared", "Accuracy"
or "Kappa")
maximize
a single logical for whether the metric
should be maximized
This function should return an integer corresponding to the optimal subset size. caret comes with two examples functions for this purpose: pickSizeBest
and pickSizeTolerance
.
After the optimal subset size is determined, the selectVar
function will be used to calculate the best rankings for each variable across all the resampling iterations. Inputs for the function are:
y
a list of variables importance for each
resampling iteration and each subset size (generated by
the user–defined rank
function). In the example,
each each of the cross–validation groups the output of
the rank
function is saved for each of the
subset sizes (including the original subset). If the
rankings are not recomputed at each iteration, the
values will be the same within each cross-validation
iteration.
size
the integer returned by the
selectSize
function
This function should return a character string of predictor names (of length size
) in the order of most important to least important
Examples of these functions are included in the package: lmFuncs
, rfFuncs
, treebagFuncs
and nbFuncs
.
Model details about these functions, including examples, are at http://caret.r-forge.r-project.org/featureselection.html. .
A list
Max Kuhn
rfe
, lmFuncs
, rfFuncs
, treebagFuncs
, nbFuncs
, pickSizeBest
, pickSizeTolerance
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ## Not run:
subsetSizes <- c(2, 4, 6, 8)
set.seed(123)
seeds <- vector(mode = "list", length = 51)
for(i in 1:50) seeds[[i]] <- sample.int(1000, length(subsetSizes) + 1)
seeds[[51]] <- sample.int(1000, 1)
set.seed(1)
rfMod <- rfe(bbbDescr, logBBB,
sizes = subsetSizes,
rfeControl = rfeControl(functions = rfFuncs,
seeds = seeds,
number = 50))
## End(Not run)
|
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