A B C D F G H K L M N P Q R S T V X
adaI | revised MLearn interface for machine learning |
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 |
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. |
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 |
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" |
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 |
hclustI | revised MLearn interface for machine learning |
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 |
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 |
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 |
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 |
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" |
predScore | Class "classifierOutput" |
predScore-method | Class "classifierOutput" |
predScores | Class "classifierOutput" |
predScores-method | Class "classifierOutput" |
probArray | MLInterfaces infrastructure |
probMat | MLInterfaces infrastructure |
qdaI | revised MLearn interface for machine learning |
qualScore | MLInterfaces infrastructure |
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 |
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 |
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" |
varImpStruct-class | Class "varImpStruct" - collect data on variable importance from various machine learning methods |
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 |