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

## Documentation for package ‘MLInterfaces’ version 1.80.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

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

## -- A --

acc | Assessing classifier performance
acc-method | Assessing classifier performance
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
brennan_2013_tabS7exc | Clinical characterization of 158 GBM samples from https://doi.org/10.1016/j.cell.2013.09.034 supp table S7

## -- C --

classifierOutput-class | Class "classifierOutput"
classifOutput | MLInterfaces infrastructure
clusteringOutput-class | container for clustering outputs in uniform structure
clusteringSchema-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.
confuTab | Compute confusion tables for a confusion matrix.
cverrs | MLInterfaces infrastructure

## -- 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

## -- E --

es2df | MLInterfaces infrastructure

## -- F --

F1 | Assessing classifier performance
F1-method | Assessing classifier performance
fn | Assessing classifier performance
fn-method | Assessing classifier performance
fp | Assessing classifier performance
fp-method | Assessing classifier performance
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 --

hclustConverter | MLInterfaces infrastructure
hclustI | revised MLearn interface for machine learning
hclustWidget | shiny-oriented GUI for cluster or classifier exploration

## -- K --

kmeansConverter | MLInterfaces infrastructure
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
learnerIn3D | Class '"projectedLearner"'
learnerIn3D-method | Class '"projectedLearner"'
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 performance
macroF1-method | Assessing classifier performance
macroF1-methods | Assessing classifier performance
makeClusteringSchema | MLInterfaces infrastructure
makeConfuMat | MLInterfaces infrastructure
makeLearnerSchema | revised MLearn interface for machine learning
mapPSvec | MLInterfaces infrastructure
membMat | MLInterfaces infrastructure
mkfmla | real adaboost (Friedman et al)
MLearn | revised MLearn interface for machine learning
MLearn-method | revised MLearn interface for machine learning
mlearnWidget | shiny-oriented GUI for cluster or classifier exploration
MLearn_new | revised MLearn interface for machine learning
MLIConverter.Bgbm | MLInterfaces infrastructure
MLIConverter.blackboost | MLInterfaces infrastructure
MLIConverter.dlda | MLInterfaces infrastructure
MLIConverter.knn | MLInterfaces infrastructure
MLIConverter.knncv | MLInterfaces infrastructure
MLIConverter.ksvm | MLInterfaces infrastructure
MLIConverter.ldaPredMeth | MLInterfaces infrastructure
MLIConverter.logistic | MLInterfaces infrastructure
MLIConverter.naiveBayes | MLInterfaces infrastructure
MLIConverter.nnet | MLInterfaces infrastructure
MLIConverter.plsda | MLInterfaces infrastructure
MLIConverter.RAB | MLInterfaces infrastructure
MLIConverter.randomForest | MLInterfaces infrastructure
MLIConverter.rpart | MLInterfaces infrastructure
MLIConverter.selftesting | MLInterfaces infrastructure
MLIConverter.slda | MLInterfaces infrastructure
MLIConverter.svm | MLInterfaces infrastructure
MLIConverterListEl.class | MLInterfaces infrastructure
MLIConverterPredType.class | MLInterfaces infrastructure
MLIPredicter.knn | MLInterfaces infrastructure
MLIPredicter.ksvm | MLInterfaces infrastructure
MLIPredicter.naiveBayes | MLInterfaces infrastructure
MLIPredicter.nnet | MLInterfaces infrastructure
MLIPredicter.plsda | MLInterfaces infrastructure
MLIPredicter.randomForest | MLInterfaces infrastructure
MLIPredicter.svm | MLInterfaces infrastructure
MLLabel | MLInterfaces infrastructure
MLOutput | MLInterfaces infrastructure
MLScore | MLInterfaces infrastructure

## -- N --

naAs0 | MLInterfaces infrastructure
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 --

pamConverter | MLInterfaces infrastructure
pamI | revised MLearn interface for machine learning
partPlot | MLInterfaces infrastructure
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'
planarPlot2 | MLInterfaces infrastructure
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 '"projectedLearner"'
plot-method | Class "varImpStruct" - collect data on variable importance from various machine learning methods
plotOne | Class '"projectedLearner"'
plotOne-method | Class '"projectedLearner"'
plotXvalRDA | revised MLearn interface for machine learning
plsda2 | revised MLearn interface for machine learning
plsdaI | revised MLearn interface for machine learning
plspinDF | MLInterfaces infrastructure
plspinHcube | shiny app for interactive 3D visualization of mlbench hypercube
prcomp-class | container for clustering outputs in uniform structure
prcompObj-class | container for clustering outputs in uniform structure
precision | Assessing classifier performance
precision-method | Assessing classifier performance
precision-methods | Assessing classifier performance
Predict | real adaboost (Friedman et al)
Predict-method | real adaboost (Friedman et al)
predict.classifierOutput | Predict method for 'classifierOutput' objects
predict.dlda2 | MLInterfaces infrastructure
predict.gbm2 | MLInterfaces infrastructure
predict.knn.cv2 | MLInterfaces infrastructure
predict.knn2 | MLInterfaces infrastructure
predict.lvq | MLInterfaces infrastructure
predict.RAB | MLInterfaces infrastructure
predictions | Class "classifierOutput"
predictions-method | Class "classifierOutput"
predScore | Class "classifierOutput"
predScore-method | Class "classifierOutput"
predScores | Class "classifierOutput"
predScores-method | Class "classifierOutput"
probArray | MLInterfaces infrastructure
probMat | MLInterfaces infrastructure
projectedLearner-class | Class '"projectedLearner"'
projectLearnerToGrid | create learned tesselation of feature space after PC transformation

## -- 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
recall | Assessing classifier performance
recall-method | Assessing classifier performance
recall-methods | Assessing classifier performance
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 --

se2df | MLInterfaces infrastructure
sensitivity | Assessing classifier performance
sensitivity-method | Assessing classifier performance
sensitivity-methods | Assessing classifier performance
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 '"projectedLearner"'
show-method | Class "raboostCont" ~~~
show-method | Class "varImpStruct" - collect data on variable importance from various machine learning methods
silhouette-class | container for clustering outputs in uniform structure
silhouetteVec | MLInterfaces infrastructure
sldaI | revised MLearn interface for machine learning
SOMBout | MLInterfaces infrastructure
somout | MLInterfaces infrastructure
specificity | Assessing classifier performance
specificity-method | Assessing classifier performance
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"
tn | Assessing classifier performance
tn-method | Assessing classifier performance
tonp | real adaboost (Friedman et al)
tp | Assessing classifier performance
tp-method | Assessing classifier performance
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 --

x | MLInterfaces infrastructure
xvalLoop | Cross-validation in clustered computing environments
xvalLoop-method | 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