Package: TunePareto 2.5.3

TunePareto: Multi-Objective Parameter Tuning for Classifiers

Generic methods for parameter tuning of classification algorithms using multiple scoring functions (Muessel et al. (2012), <doi:10.18637/jss.v046.i05>).

Authors:Christoph Müssel, Ludwig Lausser, Hans Kestler

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TunePareto.pdf |TunePareto.html
TunePareto/json (API)
NEWS

# Install 'TunePareto' in R:
install.packages('TunePareto', repos = c('https://hkestler.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.52 score 1 stars 2 packages 93 scripts 362 downloads 6 mentions 53 exports 0 dependencies

Last updated 1 years agofrom:698573680f. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 01 2024
R-4.5-win-x86_64OKNov 01 2024
R-4.5-linux-x86_64OKNov 01 2024
R-4.4-win-x86_64OKNov 01 2024
R-4.4-mac-x86_64OKNov 01 2024
R-4.4-mac-aarch64OKNov 01 2024
R-4.3-win-x86_64OKNov 01 2024
R-4.3-mac-x86_64OKNov 01 2024
R-4.3-mac-aarch64OKNov 01 2024

Exports:allCombinationsas.intervalcreateObjectivecrossValidationcvAccuracycvConfusioncvErrorcvErrorVariancecvFalloutcvFalseNegativecvFalsePositivecvMisscvNPVcvPPVcvPrecisioncvRecallcvSensitivitycvSpecificitycvTrueNegativecvTruePositivecvWeightedErrorgenerateCVRunsmergeTuneParetoResultsplotDominationGraphplotObjectivePairsplotParetoFronts2DrankByDesirabilityrecalculateParetoSetreclassAccuracyreclassConfusionreclassErrorreclassFalloutreclassFalseNegativereclassFalsePositivereclassificationreclassMissreclassNPVreclassPPVreclassPrecisionreclassRecallreclassSensitivityreclassSpecificityreclassTrueNegativereclassTruePositivereclassWeightedErrortrainTuneParetoClassifiertuneParetotunePareto.knntunePareto.NaiveBayestunePareto.randomForesttunePareto.svmtunePareto.treetuneParetoClassifier

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Multi-objective parameter tuning for classifiersTunePareto-package TunePareto
Build a list of all possible combinations of parameter valuesallCombinations
Specify a continous intervalas.interval
Create a new objective functioncreateObjective
Generate cross-validation partitionsgenerateCVRuns
Calculate optimal solutions from several calls of tuneParetomergeTuneParetoResults
Visualize the Pareto fronts of parameter configuration scoresplotDominationGraph
Plot a matrix of Pareto front panelsplotObjectivePairs
A classical 2-dimensional plot of Pareto frontsplotParetoFronts2D
Predefined precalculation functions for objectivescrossValidation precalculation reclassification
TunePareto wrappers for certain classifierspredefinedClassifiers tunePareto.knn tunePareto.NaiveBayes tunePareto.randomForest tunePareto.svm tunePareto.tree
Predefined objective functions for parameter tuningcvAccuracy cvConfusion cvError cvErrorVariance cvFallout cvFalseNegative cvFalsePositive cvMiss cvNPV cvPPV cvPrecision cvRecall cvSensitivity cvSpecificity cvTrueNegative cvTruePositive cvWeightedError predefinedObjectiveFunctions reclassAccuracy reclassConfusion reclassError reclassFallout reclassFalseNegative reclassFalsePositive reclassMiss reclassNPV reclassPPV reclassPrecision reclassRecall reclassSensitivity reclassSpecificity reclassTrueNegative reclassTruePositive reclassWeightedError
Prediction method for TuneParetoClassifier objectspredict.TuneParetoModel
Print method for objects used in TuneParetoprint.TuneParetoClassifier print.TuneParetoModel print.TuneParetoResult
Rank results according to their desirabilitiesrankByDesirability
Recalculate Pareto-optimal solutionsrecalculateParetoSet
Train a TunePareto classifiertrainTuneParetoClassifier
Generic function for multi-objective parameter tuning of classifierstunePareto
Create a classifier objecttuneParetoClassifier