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Last data update: 2014.03.03

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Results 1 - 10 of 22 found.
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MLIntInternals (Package: MLInterfaces) : MLInterfaces infrastructure

These functions are internal tools for MLInterfaces. Users will generally not call these functions directly.

● Data Source: BioConductor
● Keywords: classif
● Alias: MLLabel, MLOutput, MLScore, SOMBout, classifOutput, clustOutput, groupIndex, membMat, probArray, probMat, qualScore, silhouetteVec, somout
● 0 images

MLearn (Package: MLInterfaces) : revised MLearn interface for machine learning

revised MLearn interface for machine learning, emphasizing a schematic description of external learning functions like knn, lda, nnet, etc.

● Data Source: BioConductor
● Keywords: models
● Alias: BgbmI, MLearn, MLearn,formula,ExpressionSet,character,numeric-method, MLearn,formula,ExpressionSet,learnerSchema,numeric-method, MLearn,formula,ExpressionSet,learnerSchema,xvalSpec-method, MLearn,formula,data.frame,clusteringSchema,ANY-method, MLearn,formula,data.frame,learnerSchema,numeric-method, MLearn,formula,data.frame,learnerSchema,xvalSpec-method, MLearn_new, RABI, adaI, baggingI, blackboostI, dlda, dlda2, dldaI, gbm2, glmI.logistic, hclustI, kmeansI, knn.cv2, knn.cvI, knn2, knnI, ksvm2, ksvmI, ldaI, ldaI.predParms, lvq, lvqI, makeLearnerSchema, naiveBayesI, nnetI, pamI, plotXvalRDA, plsda2, plsdaI, qdaI, rab, randomForestI, rdaI, rdaML, rdacvI, rdacvML, rpartI, sldaI, standardMLIConverter, svm2, svmI
● 7 images

RAB (Package: MLInterfaces) : real adaboost (Friedman et al)

read adaboost ... a demonstration version

● Data Source: BioConductor
● Keywords: models
● Alias: DAB, Predict, Predict,daboostCont-method, Predict,raboostCont-method, RAB, RAB4es, mkfmla, tonp
● 0 images

balKfold.xvspec (Package: MLInterfaces) : generate a partition function for cross-validation,

generate a partition function for cross-validation, where the partitions are approximately balanced with respect to the distribution of a response variable

● Data Source: BioConductor
● Keywords: manip, models
● Alias: balKfold.xvspec
● 0 images

classifierOutput-class (Package: MLInterfaces) : Class "classifierOutput"

This class summarizes the output values from different classifiers.

● Data Source: BioConductor
● Keywords: classes
● Alias: classifierOutput-class
● 0 images

clusteringOutput-class (Package: MLInterfaces) : container for clustering outputs in uniform structure

container for clustering outputs in uniform structure

● Data Source: BioConductor
● Keywords: classes
● Alias: RObject,clusteringOutput-method, clusteringOutput-class, getConverter, getConverter,clusteringSchema-method, getDist, getDist,clusteringSchema-method, plot,clusteringOutput,ANY-method, show,clusteringOutput-method, show,clusteringSchema-method
● 0 images

confuMat-methods (Package: MLInterfaces) : Compute the confusion matrix for a classifier.

This function will compute the confusion matrix for a classifier's output

● Data Source: BioConductor
● Keywords: classif, methods
● Alias: confuMat, confuMat,classifierOutput,character-method, confuMat,classifierOutput,missing-method, confuMat,classifierOutput,numeric-method, confuMat,classifierOutput-method, confuMat-methods
● 0 images

confuTab (Package: MLInterfaces) :

Given a n by n confusion matrix, the function returns a list of n 2 by 2 tables with false positives, false negatives, false positives and true negative for each initial variables.

● Data Source: BioConductor
● Keywords:
● Alias: confuTab
● 0 images

fs.absT (Package: MLInterfaces) : support for feature selection in cross-validation

support for feature selection in cross-validation

● Data Source: BioConductor
● Keywords: models
● Alias: fs.absT, fs.probT, fs.topVariance
● 0 images

fsHistory (Package: MLInterfaces) : extract history of feature selection for a cross-validated

extract history of feature selection for a cross-validated machine learner

● Data Source: BioConductor
● Keywords: models
● Alias: fsHistory
● 0 images