A B C D F G H K L M N P Q R S T V X
acc | Assessing classifier performance |
acc-method | Assessing classifier performance |
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. |
confuTab | Compute confusion tables for a confusion matrix. |
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 |
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" |
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 |
hclustWidget | shiny-oriented GUI for cluster or classifier exploration |
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 |
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 |
macroF1 | Assessing classifier performance |
macroF1-method | Assessing classifier performance |
macroF1-methods | Assessing classifier performance |
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 |
mlearnWidget | shiny-oriented GUI for cluster or classifier exploration |
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 '"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 |
plspinHcube | shiny app for interactive 3D visualization of mlbench hypercube |
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 |
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 |
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 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 |
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 |
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 |
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" |
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 |