# Uniform interfaces to R machine learning procedures for data in Bioconductor containers

## Documentation for package ‘MLInterfaces’ version 1.36.1

- DESCRIPTION file.
- Overview of user guides and package vignettes; browse directory.

## Help Pages

A B C D F G H K L M N P Q R S T V X

## -- A --

adaI | revised MLearn interface for machine learning

## -- B --

baggingI | revised MLearn interface for machine learning
balKfold.xvspec | generate a partition function for cross-validation, where the partitions are approximately balanced with respect to the distribution of a response variable
BgbmI | revised MLearn interface for machine learning
blackboostI | revised MLearn interface for machine learning

## -- C --

classifierOutput-class | Class "classifierOutput"
classifOutput | MLInterfaces infrastructure
clusteringOutput-class | container for clustering outputs in uniform structure
clustOutput | MLInterfaces infrastructure
confuMat | Compute the confusion matrix for a classifier.
confuMat-method | Compute the confusion matrix for a classifier.
confuMat-methods | Compute the confusion matrix for a classifier.

## -- D --

DAB | real adaboost (Friedman et al)
daboostCont-class | Class "raboostCont" ~~~
dlda | revised MLearn interface for machine learning
dlda2 | revised MLearn interface for machine learning
dldaI | revised MLearn interface for machine learning

## -- F --

fs.absT | support for feature selection in cross-validation
fs.probT | support for feature selection in cross-validation
fs.topVariance | support for feature selection in cross-validation
fsHistory | extract history of feature selection for a cross-validated machine learner
fsHistory-method | Class "classifierOutput"

## -- G --

gbm2 | revised MLearn interface for machine learning
getConverter | container for clustering outputs in uniform structure
getConverter-method | container for clustering outputs in uniform structure
getDist | container for clustering outputs in uniform structure
getDist-method | container for clustering outputs in uniform structure
getGrid | MLInterfaces infrastructure
getGrid-method | MLInterfaces infrastructure
getVarImp | Class "varImpStruct" - collect data on variable importance from various machine learning methods
getVarImp-method | Class "varImpStruct" - collect data on variable importance from various machine learning methods
glmI.logistic | revised MLearn interface for machine learning
groupIndex | MLInterfaces infrastructure

## -- H --

hclustI | revised MLearn interface for machine learning

## -- K --

kmeansI | revised MLearn interface for machine learning
knn.cv2 | revised MLearn interface for machine learning
knn.cvI | revised MLearn interface for machine learning
knn2 | revised MLearn interface for machine learning
knnI | revised MLearn interface for machine learning
ksvm2 | revised MLearn interface for machine learning
ksvmI | revised MLearn interface for machine learning

## -- L --

ldaI | revised MLearn interface for machine learning
ldaI.predParms | revised MLearn interface for machine learning
learnerSchema-class | Class "learnerSchema" - convey information on a machine learning function to the MLearn wrapper
lvq | revised MLearn interface for machine learning
lvqI | revised MLearn interface for machine learning

## -- M --

macroF1 | Assessing classifier preformance
macroF1-method | Assessing classifier preformance
macroF1-methods | Assessing classifier preformance
makeLearnerSchema | revised MLearn interface for machine learning
membMat | MLInterfaces infrastructure
mkfmla | real adaboost (Friedman et al)
MLearn | revised MLearn interface for machine learning
MLearn-method | revised MLearn interface for machine learning
MLearn_new | revised MLearn interface for machine learning
MLLabel | MLInterfaces infrastructure
MLOutput | MLInterfaces infrastructure
MLScore | MLInterfaces infrastructure

## -- N --

naiveBayesI | revised MLearn interface for machine learning
nnetI | revised MLearn interface for machine learning
nonstandardLearnerSchema-class | Class "learnerSchema" - convey information on a machine learning function to the MLearn wrapper

## -- P --

pamI | revised MLearn interface for machine learning
planarPlot | Methods for Function planarPlot in Package 'MLInterfaces'
planarPlot-method | Methods for Function planarPlot in Package 'MLInterfaces'
planarPlot-methods | Methods for Function planarPlot in Package 'MLInterfaces'
plot | Class "varImpStruct" - collect data on variable importance from various machine learning methods
plot-method | container for clustering outputs in uniform structure
plot-method | Class "varImpStruct" - collect data on variable importance from various machine learning methods
plotXvalRDA | revised MLearn interface for machine learning
plsda2 | revised MLearn interface for machine learning
plsdaI | revised MLearn interface for machine learning
precision | Assessing classifier preformance
precision-method | Assessing classifier preformance
precision-methods | Assessing classifier preformance
Predict | real adaboost (Friedman et al)
Predict-method | real adaboost (Friedman et al)
predict.classifierOutput | Predict method for 'classifierOutput' objects
predictions | Class "classifierOutput"
predictions-method | Class "classifierOutput"
predScores | Class "classifierOutput"
predScores-method | Class "classifierOutput"
probArray | MLInterfaces infrastructure
probMat | MLInterfaces infrastructure

## -- Q --

qdaI | revised MLearn interface for machine learning
qualScore | MLInterfaces infrastructure

## -- R --

RAB | real adaboost (Friedman et al)
rab | revised MLearn interface for machine learning
RAB4es | real adaboost (Friedman et al)
RABI | revised MLearn interface for machine learning
raboostCont-class | Class "raboostCont" ~~~
randomForestI | revised MLearn interface for machine learning
rdacvI | revised MLearn interface for machine learning
rdacvML | revised MLearn interface for machine learning
rdaI | revised MLearn interface for machine learning
rdaML | revised MLearn interface for machine learning
recall | Assessing classifier preformance
recall-method | Assessing classifier preformance
recall-methods | Assessing classifier preformance
report | Class "varImpStruct" - collect data on variable importance from various machine learning methods
report-method | Class "varImpStruct" - collect data on variable importance from various machine learning methods
RObject | Class "classifierOutput"
RObject-method | Class "classifierOutput"
RObject-method | container for clustering outputs in uniform structure
rpartI | revised MLearn interface for machine learning

## -- S --

show-method | Class "classifierOutput"
show-method | container for clustering outputs in uniform structure
show-method | Class "learnerSchema" - convey information on a machine learning function to the MLearn wrapper
show-method | Class "raboostCont" ~~~
show-method | Class "varImpStruct" - collect data on variable importance from various machine learning methods
silhouetteVec | MLInterfaces infrastructure
sldaI | revised MLearn interface for machine learning
SOMBout | MLInterfaces infrastructure
somout | MLInterfaces infrastructure
standardMLIConverter | revised MLearn interface for machine learning
svm2 | revised MLearn interface for machine learning
svmI | revised MLearn interface for machine learning

## -- T --

testPredictions | Class "classifierOutput"
testPredictions-method | Class "classifierOutput"
testScores | Class "classifierOutput"
testScores-method | Class "classifierOutput"
tonp | real adaboost (Friedman et al)
trainInd | Class "classifierOutput"
trainInd-method | Class "classifierOutput"
trainPredictions | Class "classifierOutput"
trainPredictions-method | Class "classifierOutput"
trainScores | Class "classifierOutput"
trainScores-method | Class "classifierOutput"

## -- V --

varImpStruct-class | Class "varImpStruct" - collect data on variable importance from various machine learning methods

## -- X --

xvalLoop | Cross-validation in clustered computing environments
xvalSpec | container for information specifying a cross-validated machine learning exercise
xvalSpec-class | container for information specifying a cross-validated machine learning exercise