A B C D E 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 |
| 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 |
| 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 |
| es2df | MLInterfaces infrastructure |
| 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 |
| hclustConverter | MLInterfaces infrastructure |
| hclustI | revised MLearn interface for machine learning |
| hclustWidget | shiny-oriented GUI for cluster or classifier exploration |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| predict.rdacvML | MLInterfaces infrastructure |
| predict.rdaML | MLInterfaces infrastructure |
| predictions | Class "classifierOutput" |
| predictions-method | Class "classifierOutput" |
| predScore | Class "classifierOutput" |
| predScore-method | Class "classifierOutput" |
| predScores | Class "classifierOutput" |
| predScores-method | Class "classifierOutput" |
| print.rdacvML | MLInterfaces infrastructure |
| print.rdaML | MLInterfaces infrastructure |
| 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 |
| rda.xvalAns | MLInterfaces infrastructure |
| rdaCV | MLInterfaces infrastructure |
| rdacvI | revised MLearn interface for machine learning |
| rdacvML | revised MLearn interface for machine learning |
| rdaFixed | MLInterfaces infrastructure |
| 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 |
| 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 |
| 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 |
| 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 |