trainControl: Control parameters for train
In caret: Classification and Regression Training
Description
Usage
Arguments
Details
Value
Author(s)
References
Examples
Description
Control the computational nuances of the train function
Usage
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trainControl(method = "boot",
number = ifelse(grepl("cv", method), 10, 25),
repeats = ifelse(grepl("cv", method), 1, number),
p = 0.75,
initialWindow = NULL,
horizon = 1,
fixedWindow = TRUE,
verboseIter = FALSE,
returnData = TRUE,
returnResamp = "final",
savePredictions = FALSE,
classProbs = FALSE,
summaryFunction = defaultSummary,
selectionFunction = "best",
preProcOptions = list(thresh = 0.95, ICAcomp = 3, k = 5),
index = NULL,
indexOut = NULL,
timingSamps = 0,
predictionBounds = rep(FALSE, 2),
seeds = NA,
adaptive = list(min = 5, alpha = 0.05,
method = "gls", complete = TRUE),
allowParallel = TRUE)
Arguments
method
The resampling method: boot, boot632, cv, repeatedcv,
LOOCV, LGOCV (for repeated training/test splits), none (only fits one model to the entire training set),
oob (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis, or conditional tree forest models), "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV"
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
verboseIter
A logical for printing a training log.
returnData
A logical for saving the data
returnResamp
A character string indicating how much of the resampled summary metrics should be saved. Values can be “final”, “all” or “none”
savePredictions
a logical to save the hold-out predictions for each resample
p
For leave-group out cross-validation: the training percentage
initialWindow, horizon, fixedWindow
possible arguments to createTimeSlices
classProbs
a logical; should class probabilities be computed for classification models (along with predicted values) in each resample?
summaryFunction
a function to compute performance metrics across resamples. The arguments to the function should be the same as those in defaultSummary.
selectionFunction
the function used to select the optimal tuning parameter. This can be a name of the function or the function itself. See best for details and other options.
preProcOptions
A list of options to pass to preProcess. The type of pre-processing (e.g. center, scaling etc) is passed in via the preProc option in train.
index
a list with elements for each resampling iteration. Each list element is the sample rows used for training at that iteration.
indexOut
a list (the same length as index) that dictates which sample are held-out for each resample. If NULL, then the unique set of samples not contained in index is used.
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.
predictionBounds
a logical or numeric vector of length 2 (regression only). If logical, the predictions can be constrained to be within the limit of the training set outcomes. For example, a value of c(TRUE, FALSE) would only constrain the lower end of predictions. If numeric, specific bounds can be used. For example, if c(10, NA), values below 10 would be predicted as 10 (with no constraint in the upper side).
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 NA will stop the seed from being set within the worker processes while a value of NULL will set the seeds using a random set of integers. Alternatively, a list can be used. The list should have B+1 elements where B is the number of resamples. The first B elements of the list should be vectors of integers of length M where M is the number of models being evaluated. The last element of the list only needs to be a single integer (for the final model). See the Examples section below and the Details section.
adaptive
a list used when method is "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV". See Details below.
allowParallel
if a parallel backend is loaded and available, should the function use it?
Details
When setting the seeds manually, the number of models being evaluated is required. This may not be obvious as train does some optimizations for certain models. For example, when tuning over PLS model, the only model that is fit is the one with the largest number of components. So if the model is being tuned over comp in 1:10, the only model fit is ncomp = 10. However, if the vector of integers used in the seeds arguments is longer than actually needed, no error is thrown.
Using method = "none" and specifying model than one model in train's tuneGrid or tuneLength arguments will result in an error.
Using adaptive resampling when method is either "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV", the full set of resamples is not run for each model. As resampling continues, a futility analysis is conducted and models with a low probability of being optimal are removed. These features are experimental. See Kuhn (2014) for more details. The options for this procedure are:
-
min: the minimum number of resamples used before models are removed
-
alpha: the confidence level of the one-sided intervals used to measure futility
-
method: either generalized least squares (method = "gls") or a Bradley-Terry model (method = "BT")
-
complete: if a single parameter value is found before the end of resampling, should the full set of resamples be computed for that parameter. )
Value
An echo of the parameters specified
Author(s)
Max Kuhn
References
Kuhn (2014), “Futility Analysis in the Cross-Validation of Machine Learning Models” http://arxiv.org/abs/1405.6974
Examples
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## Not run:
## Do 5 repeats of 10-Fold CV for the iris data. We will fit
## a KNN model that evaluates 12 values of k and set the seed
## at each iteration.
set.seed(123)
seeds <- vector(mode = "list", length = 51)
for(i in 1:50) seeds[[i]] <- sample.int(1000, 22)
## For the last model:
seeds[[51]] <- sample.int(1000, 1)
ctrl <- trainControl(method = "repeatedcv",
repeats = 5,
seeds = seeds)
set.seed(1)
mod <- train(Species ~ ., data = iris,
method = "knn",
tuneLength = 12,
trControl = ctrl)
ctrl2 <- trainControl(method = "adaptive_cv",
repeats = 5,
verboseIter = TRUE,
seeds = seeds)
set.seed(1)
mod2 <- train(Species ~ ., data = iris,
method = "knn",
tuneLength = 12,
trControl = ctrl2)
## End(Not run)
Example output
Loading required package: lattice
Loading required package: ggplot2
+ Fold01.Rep1: k= 5
- Fold01.Rep1: k= 5
+ Fold01.Rep1: k= 7
- Fold01.Rep1: k= 7
+ Fold01.Rep1: k= 9
- Fold01.Rep1: k= 9
+ Fold01.Rep1: k=11
- Fold01.Rep1: k=11
+ Fold01.Rep1: k=13
- Fold01.Rep1: k=13
+ Fold01.Rep1: k=15
- Fold01.Rep1: k=15
+ Fold01.Rep1: k=17
- Fold01.Rep1: k=17
+ Fold01.Rep1: k=19
- Fold01.Rep1: k=19
+ Fold01.Rep1: k=21
- Fold01.Rep1: k=21
+ Fold01.Rep1: k=23
- Fold01.Rep1: k=23
+ Fold01.Rep1: k=25
- Fold01.Rep1: k=25
+ Fold01.Rep1: k=27
- Fold01.Rep1: k=27
+ Fold02.Rep1: k= 5
- Fold02.Rep1: k= 5
+ Fold02.Rep1: k= 7
- Fold02.Rep1: k= 7
+ Fold02.Rep1: k= 9
- Fold02.Rep1: k= 9
+ Fold02.Rep1: k=11
- Fold02.Rep1: k=11
+ Fold02.Rep1: k=13
- Fold02.Rep1: k=13
+ Fold02.Rep1: k=15
- Fold02.Rep1: k=15
+ Fold02.Rep1: k=17
- Fold02.Rep1: k=17
+ Fold02.Rep1: k=19
- Fold02.Rep1: k=19
+ Fold02.Rep1: k=21
- Fold02.Rep1: k=21
+ Fold02.Rep1: k=23
- Fold02.Rep1: k=23
+ Fold02.Rep1: k=25
- Fold02.Rep1: k=25
+ Fold02.Rep1: k=27
- Fold02.Rep1: k=27
+ Fold03.Rep1: k= 5
- Fold03.Rep1: k= 5
+ Fold03.Rep1: k= 7
- Fold03.Rep1: k= 7
+ Fold03.Rep1: k= 9
- Fold03.Rep1: k= 9
+ Fold03.Rep1: k=11
- Fold03.Rep1: k=11
+ Fold03.Rep1: k=13
- Fold03.Rep1: k=13
+ Fold03.Rep1: k=15
- Fold03.Rep1: k=15
+ Fold03.Rep1: k=17
- Fold03.Rep1: k=17
+ Fold03.Rep1: k=19
- Fold03.Rep1: k=19
+ Fold03.Rep1: k=21
- Fold03.Rep1: k=21
+ Fold03.Rep1: k=23
- Fold03.Rep1: k=23
+ Fold03.Rep1: k=25
- Fold03.Rep1: k=25
+ Fold03.Rep1: k=27
- Fold03.Rep1: k=27
+ Fold04.Rep1: k= 5
- Fold04.Rep1: k= 5
+ Fold04.Rep1: k= 7
- Fold04.Rep1: k= 7
+ Fold04.Rep1: k= 9
- Fold04.Rep1: k= 9
+ Fold04.Rep1: k=11
- Fold04.Rep1: k=11
+ Fold04.Rep1: k=13
- Fold04.Rep1: k=13
+ Fold04.Rep1: k=15
- Fold04.Rep1: k=15
+ Fold04.Rep1: k=17
- Fold04.Rep1: k=17
+ Fold04.Rep1: k=19
- Fold04.Rep1: k=19
+ Fold04.Rep1: k=21
- Fold04.Rep1: k=21
+ Fold04.Rep1: k=23
- Fold04.Rep1: k=23
+ Fold04.Rep1: k=25
- Fold04.Rep1: k=25
+ Fold04.Rep1: k=27
- Fold04.Rep1: k=27
+ Fold05.Rep1: k= 5
- Fold05.Rep1: k= 5
+ Fold05.Rep1: k= 7
- Fold05.Rep1: k= 7
+ Fold05.Rep1: k= 9
- Fold05.Rep1: k= 9
+ Fold05.Rep1: k=11
- Fold05.Rep1: k=11
+ Fold05.Rep1: k=13
- Fold05.Rep1: k=13
+ Fold05.Rep1: k=15
- Fold05.Rep1: k=15
+ Fold05.Rep1: k=17
- Fold05.Rep1: k=17
+ Fold05.Rep1: k=19
- Fold05.Rep1: k=19
+ Fold05.Rep1: k=21
- Fold05.Rep1: k=21
+ Fold05.Rep1: k=23
- Fold05.Rep1: k=23
+ Fold05.Rep1: k=25
- Fold05.Rep1: k=25
+ Fold05.Rep1: k=27
- Fold05.Rep1: k=27
o 3 eliminated;9 remain
+ Fold06.Rep1: k= 9
- Fold06.Rep1: k= 9
+ Fold06.Rep1: k=11
- Fold06.Rep1: k=11
+ Fold06.Rep1: k=13
- Fold06.Rep1: k=13
+ Fold06.Rep1: k=15
- Fold06.Rep1: k=15
+ Fold06.Rep1: k=17
- Fold06.Rep1: k=17
+ Fold06.Rep1: k=19
- Fold06.Rep1: k=19
+ Fold06.Rep1: k=21
- Fold06.Rep1: k=21
+ Fold06.Rep1: k=23
- Fold06.Rep1: k=23
+ Fold06.Rep1: k=25
- Fold06.Rep1: k=25
o 8 models of 9 were eliminated due to linear dependencies
x parameter filtering failed:[1] "Error in t.test.default(x2$value, alternative = \"greater\") : \n not enough 'x' observations\n"
attr(,"class")
[1] "try-error"
attr(,"condition")
<simpleError in t.test.default(x2$value, alternative = "greater"): not enough 'x' observations>
o 1 eliminated;0 remains
o 1 eliminated;8 remain
+ Fold08.Rep1: k= 9
- Fold08.Rep1: k= 9
+ Fold08.Rep1: k=11
- Fold08.Rep1: k=11
+ Fold08.Rep1: k=13
- Fold08.Rep1: k=13
+ Fold08.Rep1: k=15
- Fold08.Rep1: k=15
+ Fold08.Rep1: k=17
- Fold08.Rep1: k=17
+ Fold08.Rep1: k=19
- Fold08.Rep1: k=19
+ Fold08.Rep1: k=21
- Fold08.Rep1: k=21
+ Fold08.Rep1: k=25
- Fold08.Rep1: k=25
o 1 eliminated;7 remain
+ Fold09.Rep1: k= 9
- Fold09.Rep1: k= 9
+ Fold09.Rep1: k=11
- Fold09.Rep1: k=11
+ Fold09.Rep1: k=13
- Fold09.Rep1: k=13
+ Fold09.Rep1: k=15
- Fold09.Rep1: k=15
+ Fold09.Rep1: k=17
- Fold09.Rep1: k=17
+ Fold09.Rep1: k=19
- Fold09.Rep1: k=19
+ Fold09.Rep1: k=21
- Fold09.Rep1: k=21
o 6 eliminated;1 remains
+ Fold10.Rep1: k=13
- Fold10.Rep1: k=13
+ Fold01.Rep2: k=13
- Fold01.Rep2: k=13
+ Fold02.Rep2: k=13
- Fold02.Rep2: k=13
+ Fold03.Rep2: k=13
- Fold03.Rep2: k=13
+ Fold04.Rep2: k=13
- Fold04.Rep2: k=13
+ Fold05.Rep2: k=13
- Fold05.Rep2: k=13
+ Fold06.Rep2: k=13
- Fold06.Rep2: k=13
+ Fold07.Rep2: k=13
- Fold07.Rep2: k=13
+ Fold08.Rep2: k=13
- Fold08.Rep2: k=13
+ Fold09.Rep2: k=13
- Fold09.Rep2: k=13
+ Fold10.Rep2: k=13
- Fold10.Rep2: k=13
+ Fold01.Rep3: k=13
- Fold01.Rep3: k=13
+ Fold02.Rep3: k=13
- Fold02.Rep3: k=13
+ Fold03.Rep3: k=13
- Fold03.Rep3: k=13
+ Fold04.Rep3: k=13
- Fold04.Rep3: k=13
+ Fold05.Rep3: k=13
- Fold05.Rep3: k=13
+ Fold06.Rep3: k=13
- Fold06.Rep3: k=13
+ Fold07.Rep3: k=13
- Fold07.Rep3: k=13
+ Fold08.Rep3: k=13
- Fold08.Rep3: k=13
+ Fold09.Rep3: k=13
- Fold09.Rep3: k=13
+ Fold10.Rep3: k=13
- Fold10.Rep3: k=13
+ Fold01.Rep4: k=13
- Fold01.Rep4: k=13
+ Fold02.Rep4: k=13
- Fold02.Rep4: k=13
+ Fold03.Rep4: k=13
- Fold03.Rep4: k=13
+ Fold04.Rep4: k=13
- Fold04.Rep4: k=13
+ Fold05.Rep4: k=13
- Fold05.Rep4: k=13
+ Fold06.Rep4: k=13
- Fold06.Rep4: k=13
+ Fold07.Rep4: k=13
- Fold07.Rep4: k=13
+ Fold08.Rep4: k=13
- Fold08.Rep4: k=13
+ Fold09.Rep4: k=13
- Fold09.Rep4: k=13
+ Fold10.Rep4: k=13
- Fold10.Rep4: k=13
+ Fold01.Rep5: k=13
- Fold01.Rep5: k=13
+ Fold02.Rep5: k=13
- Fold02.Rep5: k=13
+ Fold03.Rep5: k=13
- Fold03.Rep5: k=13
+ Fold04.Rep5: k=13
- Fold04.Rep5: k=13
+ Fold05.Rep5: k=13
- Fold05.Rep5: k=13
+ Fold06.Rep5: k=13
- Fold06.Rep5: k=13
+ Fold07.Rep5: k=13
- Fold07.Rep5: k=13
+ Fold08.Rep5: k=13
- Fold08.Rep5: k=13
+ Fold09.Rep5: k=13
- Fold09.Rep5: k=13
+ Fold10.Rep5: k=13
- Fold10.Rep5: k=13
Aggregating results
Selecting tuning parameters
Fitting k = 13 on full training set
caret documentation built on May 2, 2019, 5:47 p.m.
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Description Usage Arguments Details Value Author(s) References Examples
Description
Control the computational nuances of the train function
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | trainControl(method = "boot",
number = ifelse(grepl("cv", method), 10, 25),
repeats = ifelse(grepl("cv", method), 1, number),
p = 0.75,
initialWindow = NULL,
horizon = 1,
fixedWindow = TRUE,
verboseIter = FALSE,
returnData = TRUE,
returnResamp = "final",
savePredictions = FALSE,
classProbs = FALSE,
summaryFunction = defaultSummary,
selectionFunction = "best",
preProcOptions = list(thresh = 0.95, ICAcomp = 3, k = 5),
index = NULL,
indexOut = NULL,
timingSamps = 0,
predictionBounds = rep(FALSE, 2),
seeds = NA,
adaptive = list(min = 5, alpha = 0.05,
method = "gls", complete = TRUE),
allowParallel = TRUE)
|
Arguments
method |
The 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 |
verboseIter |
A logical for printing a training log. |
returnData |
A logical for saving the data |
returnResamp |
A character string indicating how much of the resampled summary metrics should be saved. Values can be “final”, “all” or “none” |
savePredictions |
a logical to save the hold-out predictions for each resample |
p |
For leave-group out cross-validation: the training percentage |
initialWindow, horizon, fixedWindow |
possible arguments to |
classProbs |
a logical; should class probabilities be computed for classification models (along with predicted values) in each resample? |
summaryFunction |
a function to compute performance metrics across resamples. The arguments to the function should be the same as those in |
selectionFunction |
the function used to select the optimal tuning parameter. This can be a name of the function or the function itself. See |
preProcOptions |
A list of options to pass to |
index |
a list with elements for each resampling iteration. Each list element is the sample rows used for training at that iteration. |
indexOut |
a list (the same length as |
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. |
predictionBounds |
a logical or numeric vector of length 2 (regression only). If logical, the predictions can be constrained to be within the limit of the training set outcomes. For example, a value of |
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 |
adaptive |
a list used when |
allowParallel |
if a parallel backend is loaded and available, should the function use it? |
Details
When setting the seeds manually, the number of models being evaluated is required. This may not be obvious as train does some optimizations for certain models. For example, when tuning over PLS model, the only model that is fit is the one with the largest number of components. So if the model is being tuned over comp in 1:10, the only model fit is ncomp = 10. However, if the vector of integers used in the seeds arguments is longer than actually needed, no error is thrown.
Using method = "none" and specifying model than one model in train's tuneGrid or tuneLength arguments will result in an error.
Using adaptive resampling when method is either "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV", the full set of resamples is not run for each model. As resampling continues, a futility analysis is conducted and models with a low probability of being optimal are removed. These features are experimental. See Kuhn (2014) for more details. The options for this procedure are:
-
min: the minimum number of resamples used before models are removed -
alpha: the confidence level of the one-sided intervals used to measure futility -
method: either generalized least squares (method = "gls") or a Bradley-Terry model (method = "BT") -
complete: if a single parameter value is found before the end of resampling, should the full set of resamples be computed for that parameter. )
Value
An echo of the parameters specified
Author(s)
Max Kuhn
References
Kuhn (2014), “Futility Analysis in the Cross-Validation of Machine Learning Models” http://arxiv.org/abs/1405.6974
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ## Not run:
## Do 5 repeats of 10-Fold CV for the iris data. We will fit
## a KNN model that evaluates 12 values of k and set the seed
## at each iteration.
set.seed(123)
seeds <- vector(mode = "list", length = 51)
for(i in 1:50) seeds[[i]] <- sample.int(1000, 22)
## For the last model:
seeds[[51]] <- sample.int(1000, 1)
ctrl <- trainControl(method = "repeatedcv",
repeats = 5,
seeds = seeds)
set.seed(1)
mod <- train(Species ~ ., data = iris,
method = "knn",
tuneLength = 12,
trControl = ctrl)
ctrl2 <- trainControl(method = "adaptive_cv",
repeats = 5,
verboseIter = TRUE,
seeds = seeds)
set.seed(1)
mod2 <- train(Species ~ ., data = iris,
method = "knn",
tuneLength = 12,
trControl = ctrl2)
## End(Not run)
|
Example output
Loading required package: lattice
Loading required package: ggplot2
+ Fold01.Rep1: k= 5
- Fold01.Rep1: k= 5
+ Fold01.Rep1: k= 7
- Fold01.Rep1: k= 7
+ Fold01.Rep1: k= 9
- Fold01.Rep1: k= 9
+ Fold01.Rep1: k=11
- Fold01.Rep1: k=11
+ Fold01.Rep1: k=13
- Fold01.Rep1: k=13
+ Fold01.Rep1: k=15
- Fold01.Rep1: k=15
+ Fold01.Rep1: k=17
- Fold01.Rep1: k=17
+ Fold01.Rep1: k=19
- Fold01.Rep1: k=19
+ Fold01.Rep1: k=21
- Fold01.Rep1: k=21
+ Fold01.Rep1: k=23
- Fold01.Rep1: k=23
+ Fold01.Rep1: k=25
- Fold01.Rep1: k=25
+ Fold01.Rep1: k=27
- Fold01.Rep1: k=27
+ Fold02.Rep1: k= 5
- Fold02.Rep1: k= 5
+ Fold02.Rep1: k= 7
- Fold02.Rep1: k= 7
+ Fold02.Rep1: k= 9
- Fold02.Rep1: k= 9
+ Fold02.Rep1: k=11
- Fold02.Rep1: k=11
+ Fold02.Rep1: k=13
- Fold02.Rep1: k=13
+ Fold02.Rep1: k=15
- Fold02.Rep1: k=15
+ Fold02.Rep1: k=17
- Fold02.Rep1: k=17
+ Fold02.Rep1: k=19
- Fold02.Rep1: k=19
+ Fold02.Rep1: k=21
- Fold02.Rep1: k=21
+ Fold02.Rep1: k=23
- Fold02.Rep1: k=23
+ Fold02.Rep1: k=25
- Fold02.Rep1: k=25
+ Fold02.Rep1: k=27
- Fold02.Rep1: k=27
+ Fold03.Rep1: k= 5
- Fold03.Rep1: k= 5
+ Fold03.Rep1: k= 7
- Fold03.Rep1: k= 7
+ Fold03.Rep1: k= 9
- Fold03.Rep1: k= 9
+ Fold03.Rep1: k=11
- Fold03.Rep1: k=11
+ Fold03.Rep1: k=13
- Fold03.Rep1: k=13
+ Fold03.Rep1: k=15
- Fold03.Rep1: k=15
+ Fold03.Rep1: k=17
- Fold03.Rep1: k=17
+ Fold03.Rep1: k=19
- Fold03.Rep1: k=19
+ Fold03.Rep1: k=21
- Fold03.Rep1: k=21
+ Fold03.Rep1: k=23
- Fold03.Rep1: k=23
+ Fold03.Rep1: k=25
- Fold03.Rep1: k=25
+ Fold03.Rep1: k=27
- Fold03.Rep1: k=27
+ Fold04.Rep1: k= 5
- Fold04.Rep1: k= 5
+ Fold04.Rep1: k= 7
- Fold04.Rep1: k= 7
+ Fold04.Rep1: k= 9
- Fold04.Rep1: k= 9
+ Fold04.Rep1: k=11
- Fold04.Rep1: k=11
+ Fold04.Rep1: k=13
- Fold04.Rep1: k=13
+ Fold04.Rep1: k=15
- Fold04.Rep1: k=15
+ Fold04.Rep1: k=17
- Fold04.Rep1: k=17
+ Fold04.Rep1: k=19
- Fold04.Rep1: k=19
+ Fold04.Rep1: k=21
- Fold04.Rep1: k=21
+ Fold04.Rep1: k=23
- Fold04.Rep1: k=23
+ Fold04.Rep1: k=25
- Fold04.Rep1: k=25
+ Fold04.Rep1: k=27
- Fold04.Rep1: k=27
+ Fold05.Rep1: k= 5
- Fold05.Rep1: k= 5
+ Fold05.Rep1: k= 7
- Fold05.Rep1: k= 7
+ Fold05.Rep1: k= 9
- Fold05.Rep1: k= 9
+ Fold05.Rep1: k=11
- Fold05.Rep1: k=11
+ Fold05.Rep1: k=13
- Fold05.Rep1: k=13
+ Fold05.Rep1: k=15
- Fold05.Rep1: k=15
+ Fold05.Rep1: k=17
- Fold05.Rep1: k=17
+ Fold05.Rep1: k=19
- Fold05.Rep1: k=19
+ Fold05.Rep1: k=21
- Fold05.Rep1: k=21
+ Fold05.Rep1: k=23
- Fold05.Rep1: k=23
+ Fold05.Rep1: k=25
- Fold05.Rep1: k=25
+ Fold05.Rep1: k=27
- Fold05.Rep1: k=27
o 3 eliminated;9 remain
+ Fold06.Rep1: k= 9
- Fold06.Rep1: k= 9
+ Fold06.Rep1: k=11
- Fold06.Rep1: k=11
+ Fold06.Rep1: k=13
- Fold06.Rep1: k=13
+ Fold06.Rep1: k=15
- Fold06.Rep1: k=15
+ Fold06.Rep1: k=17
- Fold06.Rep1: k=17
+ Fold06.Rep1: k=19
- Fold06.Rep1: k=19
+ Fold06.Rep1: k=21
- Fold06.Rep1: k=21
+ Fold06.Rep1: k=23
- Fold06.Rep1: k=23
+ Fold06.Rep1: k=25
- Fold06.Rep1: k=25
o 8 models of 9 were eliminated due to linear dependencies
x parameter filtering failed:[1] "Error in t.test.default(x2$value, alternative = \"greater\") : \n not enough 'x' observations\n"
attr(,"class")
[1] "try-error"
attr(,"condition")
<simpleError in t.test.default(x2$value, alternative = "greater"): not enough 'x' observations>
o 1 eliminated;0 remains
o 1 eliminated;8 remain
+ Fold08.Rep1: k= 9
- Fold08.Rep1: k= 9
+ Fold08.Rep1: k=11
- Fold08.Rep1: k=11
+ Fold08.Rep1: k=13
- Fold08.Rep1: k=13
+ Fold08.Rep1: k=15
- Fold08.Rep1: k=15
+ Fold08.Rep1: k=17
- Fold08.Rep1: k=17
+ Fold08.Rep1: k=19
- Fold08.Rep1: k=19
+ Fold08.Rep1: k=21
- Fold08.Rep1: k=21
+ Fold08.Rep1: k=25
- Fold08.Rep1: k=25
o 1 eliminated;7 remain
+ Fold09.Rep1: k= 9
- Fold09.Rep1: k= 9
+ Fold09.Rep1: k=11
- Fold09.Rep1: k=11
+ Fold09.Rep1: k=13
- Fold09.Rep1: k=13
+ Fold09.Rep1: k=15
- Fold09.Rep1: k=15
+ Fold09.Rep1: k=17
- Fold09.Rep1: k=17
+ Fold09.Rep1: k=19
- Fold09.Rep1: k=19
+ Fold09.Rep1: k=21
- Fold09.Rep1: k=21
o 6 eliminated;1 remains
+ Fold10.Rep1: k=13
- Fold10.Rep1: k=13
+ Fold01.Rep2: k=13
- Fold01.Rep2: k=13
+ Fold02.Rep2: k=13
- Fold02.Rep2: k=13
+ Fold03.Rep2: k=13
- Fold03.Rep2: k=13
+ Fold04.Rep2: k=13
- Fold04.Rep2: k=13
+ Fold05.Rep2: k=13
- Fold05.Rep2: k=13
+ Fold06.Rep2: k=13
- Fold06.Rep2: k=13
+ Fold07.Rep2: k=13
- Fold07.Rep2: k=13
+ Fold08.Rep2: k=13
- Fold08.Rep2: k=13
+ Fold09.Rep2: k=13
- Fold09.Rep2: k=13
+ Fold10.Rep2: k=13
- Fold10.Rep2: k=13
+ Fold01.Rep3: k=13
- Fold01.Rep3: k=13
+ Fold02.Rep3: k=13
- Fold02.Rep3: k=13
+ Fold03.Rep3: k=13
- Fold03.Rep3: k=13
+ Fold04.Rep3: k=13
- Fold04.Rep3: k=13
+ Fold05.Rep3: k=13
- Fold05.Rep3: k=13
+ Fold06.Rep3: k=13
- Fold06.Rep3: k=13
+ Fold07.Rep3: k=13
- Fold07.Rep3: k=13
+ Fold08.Rep3: k=13
- Fold08.Rep3: k=13
+ Fold09.Rep3: k=13
- Fold09.Rep3: k=13
+ Fold10.Rep3: k=13
- Fold10.Rep3: k=13
+ Fold01.Rep4: k=13
- Fold01.Rep4: k=13
+ Fold02.Rep4: k=13
- Fold02.Rep4: k=13
+ Fold03.Rep4: k=13
- Fold03.Rep4: k=13
+ Fold04.Rep4: k=13
- Fold04.Rep4: k=13
+ Fold05.Rep4: k=13
- Fold05.Rep4: k=13
+ Fold06.Rep4: k=13
- Fold06.Rep4: k=13
+ Fold07.Rep4: k=13
- Fold07.Rep4: k=13
+ Fold08.Rep4: k=13
- Fold08.Rep4: k=13
+ Fold09.Rep4: k=13
- Fold09.Rep4: k=13
+ Fold10.Rep4: k=13
- Fold10.Rep4: k=13
+ Fold01.Rep5: k=13
- Fold01.Rep5: k=13
+ Fold02.Rep5: k=13
- Fold02.Rep5: k=13
+ Fold03.Rep5: k=13
- Fold03.Rep5: k=13
+ Fold04.Rep5: k=13
- Fold04.Rep5: k=13
+ Fold05.Rep5: k=13
- Fold05.Rep5: k=13
+ Fold06.Rep5: k=13
- Fold06.Rep5: k=13
+ Fold07.Rep5: k=13
- Fold07.Rep5: k=13
+ Fold08.Rep5: k=13
- Fold08.Rep5: k=13
+ Fold09.Rep5: k=13
- Fold09.Rep5: k=13
+ Fold10.Rep5: k=13
- Fold10.Rep5: k=13
Aggregating results
Selecting tuning parameters
Fitting k = 13 on full training set
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