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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,classifierOutput,character-method | Compute the confusion matrix for a classifier. |
| confuMat,classifierOutput,missing-method | Compute the confusion matrix for a classifier. |
| confuMat,classifierOutput-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,classifierOutput-method | Class "classifierOutput" |
| gbm2 | revised MLearn interface for machine learning |
| getConverter | container for clustering outputs in uniform structure |
| getConverter,clusteringSchema-method | container for clustering outputs in uniform structure |
| getDist | container for clustering outputs in uniform structure |
| getDist,clusteringSchema-method | container for clustering outputs in uniform structure |
| getGrid | MLInterfaces infrastructure |
| getGrid,data.frame-method | MLInterfaces infrastructure |
| getGrid,ExpressionSet-method | MLInterfaces infrastructure |
| getVarImp | Class "varImpStruct" - collect data on variable importance from various machine learning methods |
| getVarImp,classifierOutput,logical-method | Class "varImpStruct" - collect data on variable importance from various machine learning methods |
| getVarImp,classifierOutput,missing-method | Class "varImpStruct" - collect data on variable importance from various machine learning methods |
| getVarImp,classifOutput,logical-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 |
| 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 |
| makeLearnerSchema | revised MLearn interface for machine learning |
| membMat | MLInterfaces infrastructure |
| mkfmla | real adaboost (Friedman et al) |
| MLearn | revised MLearn interface for machine learning |
| MLearn,formula,data.frame,clusteringSchema,ANY-method | revised MLearn interface for machine learning |
| MLearn,formula,data.frame,learnerSchema,numeric-method | revised MLearn interface for machine learning |
| MLearn,formula,data.frame,learnerSchema,xvalSpec-method | revised MLearn interface for machine learning |
| MLearn,formula,ExpressionSet,character,numeric-method | revised MLearn interface for machine learning |
| MLearn,formula,ExpressionSet,learnerSchema,numeric-method | revised MLearn interface for machine learning |
| MLearn,formula,ExpressionSet,learnerSchema,xvalSpec-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,classifierOutput,data.frame,character-method | Methods for Function planarPlot in Package 'MLInterfaces' |
| planarPlot,classifierOutput,ExpressionSet,character-method | Methods for Function planarPlot in Package 'MLInterfaces' |
| planarPlot-methods | Methods for Function planarPlot in Package 'MLInterfaces' |
| plot,clusteringOutput,ANY-method | container for clustering outputs in uniform structure |
| plot,varImpStruct,ANY-method | Class "varImpStruct" - collect data on variable importance from various machine learning methods |
| plot,varImpStruct-method | Class "varImpStruct" - collect data on variable importance from various machine learning methods |
| plotXvalRDA | revised MLearn interface for machine learning |
| Predict | real adaboost (Friedman et al) |
| Predict,daboostCont-method | real adaboost (Friedman et al) |
| Predict,raboostCont-method | real adaboost (Friedman et al) |
| 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 |
| report | Class "varImpStruct" - collect data on variable importance from various machine learning methods |
| report,varImpStruct-method | Class "varImpStruct" - collect data on variable importance from various machine learning methods |
| RObject | Class "classifierOutput" |
| RObject,classifierOutput-method | Class "classifierOutput" |
| RObject,clusteringOutput-method | container for clustering outputs in uniform structure |
| rpartI | revised MLearn interface for machine learning |
| show,classifierOutput-method | Class "classifierOutput" |
| show,clusteringOutput-method | container for clustering outputs in uniform structure |
| show,clusteringSchema-method | container for clustering outputs in uniform structure |
| show,learnerSchema-method | Class "learnerSchema" - convey information on a machine learning function to the MLearn wrapper |
| show,raboostCont-method | Class "raboostCont" ~~~ |
| show,varImpStruct-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 |
| svmI | revised MLearn interface for machine learning |
| testPredictions | Class "classifierOutput" |
| testPredictions,classifierOutput-method | Class "classifierOutput" |
| testScores | Class "classifierOutput" |
| testScores,classifierOutput-method | Class "classifierOutput" |
| tonp | real adaboost (Friedman et al) |
| trainPredictions | Class "classifierOutput" |
| trainPredictions,classifierOutput-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 |