TwoClass {D2MCS}

Control parameters for train stage (Bi-class problem).

Description

Implementation to control the computational nuances of train function for bi-class problems.

Super class

D2MCS::TrainFunction -> TwoClass

Methods

Public methods

• TwoClass$new()

• TwoClass$create()

• TwoClass$getTrFunction()

• TwoClass$setClassProbs()

• TwoClass$getMeasures()

• TwoClass$getType()

• TwoClass$setSummaryFunction()

• TwoClass$clone()

Inherited methods

• D2MCS::TrainFunction$getAllowParallel()
• D2MCS::TrainFunction$getClassProbs()
• D2MCS::TrainFunction$getNumberFolds()
• D2MCS::TrainFunction$getResamplingMethod()
• D2MCS::TrainFunction$getSavePredictions()
• D2MCS::TrainFunction$getSeed()
• D2MCS::TrainFunction$getVerboseIter()

Method new()

Usage

TwoClass$new(
  method,
  number,
  savePredictions,
  classProbs,
  allowParallel,
  verboseIter,
  seed = NULL
)

Arguments

method

The resampling method: "boot", "boot632", "optimism_boot", "boot_all", "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), timeslice, "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV"

number

Either the number of folds or number of resampling iterations

savePredictions

An indicator of how much of the hold-out predictions for each resample should be saved. Values can be either "all", "final", or "none". A logical value can also be used that convert to "all" (for true) or "none" (for false). "final" saves the predictions for the optimal tuning parameters.

classProbs

A logical value. Should class probabilities be computed for classification models (along with predicted values) in each resample?

allowParallel

A logical value. If a parallel backend is loaded and available, should the function use it?

verboseIter

A logical for printing a training log.

seed

An optional integer that will be used to set the seed during model training stage.

Method create()

Creates a trainControl requires for the training stage.

Usage

TwoClass$create(summaryFunction, search.method = "grid", class.probs = NULL)

Arguments

summaryFunction

An object inherited from SummaryFunction class.

search.method

Either "grid" or "random", describing how the tuning parameter grid is determined.

class.probs

A logical indicating if class probabilities should be computed for classification models (along with predicted values) in each resample

Method getTrFunction()

Function used to return the trainControl object.

Usage

TwoClass$getTrFunction()

Returns

A trainControl object.

Method setClassProbs()

The function allows changing the class computation capabilities.

Usage

TwoClass$setClassProbs(class.probs)

Arguments

class.probs

A logical value. TRUE implies classification probabilities should be computed for classification models and FALSE otherwise.

Method getMeasures()

Returns the measures used to optimize model hyperparameters.

Usage

TwoClass$getMeasures()

Returns

A character vector.

Method getType()

Obtains the type of classification problem ("Bi-class" or "Multi-class").

Usage

TwoClass$getType()

Returns

A character vector with "Bi-class" value.

Method setSummaryFunction()

Function used to change the SummaryFunction used in the training stage.

Usage

TwoClass$setSummaryFunction(summaryFunction)

Arguments

summaryFunction

An object inherited from SummaryFunction class.

Method clone()

The objects of this class are cloneable with this method.

Usage

TwoClass$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

TrainFunction