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:
TunePareto_2.5.3.tar.gz
TunePareto_2.5.3.zip(r-4.7)TunePareto_2.5.3.zip(r-4.6)TunePareto_2.5.3.zip(r-4.5)
TunePareto_2.5.3.tgz(r-4.6-x86_64)TunePareto_2.5.3.tgz(r-4.6-arm64)TunePareto_2.5.3.tgz(r-4.5-x86_64)TunePareto_2.5.3.tgz(r-4.5-arm64)
TunePareto_2.5.3.tar.gz(r-4.7-arm64)TunePareto_2.5.3.tar.gz(r-4.7-x86_64)TunePareto_2.5.3.tar.gz(r-4.6-arm64)TunePareto_2.5.3.tar.gz(r-4.6-x86_64)
TunePareto_2.5.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
TunePareto/json (API)
| # Install 'TunePareto' in R: |
| install.packages('TunePareto', repos = c('https://hkestler.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:698573680f. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 180 | ||
| linux-devel-x86_64 | OK | 132 | ||
| source / vignettes | OK | 177 | ||
| linux-release-arm64 | OK | 168 | ||
| linux-release-x86_64 | OK | 141 | ||
| macos-release-arm64 | OK | 93 | ||
| macos-release-x86_64 | OK | 276 | ||
| macos-oldrel-arm64 | OK | 103 | ||
| macos-oldrel-x86_64 | OK | 198 | ||
| windows-devel | OK | 85 | ||
| windows-release | OK | 89 | ||
| windows-oldrel | OK | 92 | ||
| wasm-release | OK | 141 |
Exports:allCombinationsas.intervalcreateObjectivecrossValidationcvAccuracycvConfusioncvErrorcvErrorVariancecvFalloutcvFalseNegativecvFalsePositivecvMisscvNPVcvPPVcvPrecisioncvRecallcvSensitivitycvSpecificitycvTrueNegativecvTruePositivecvWeightedErrorgenerateCVRunsmergeTuneParetoResultsplotDominationGraphplotObjectivePairsplotParetoFronts2DrankByDesirabilityrecalculateParetoSetreclassAccuracyreclassConfusionreclassErrorreclassFalloutreclassFalseNegativereclassFalsePositivereclassificationreclassMissreclassNPVreclassPPVreclassPrecisionreclassRecallreclassSensitivityreclassSpecificityreclassTrueNegativereclassTruePositivereclassWeightedErrortrainTuneParetoClassifiertuneParetotunePareto.knntunePareto.NaiveBayestunePareto.randomForesttunePareto.svmtunePareto.treetuneParetoClassifier
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Multi-objective parameter tuning for classifiers | TunePareto-package TunePareto |
| Build a list of all possible combinations of parameter values | allCombinations |
| Specify a continous interval | as.interval |
| Create a new objective function | createObjective |
| Generate cross-validation partitions | generateCVRuns |
| Calculate optimal solutions from several calls of tunePareto | mergeTuneParetoResults |
| Visualize the Pareto fronts of parameter configuration scores | plotDominationGraph |
| Plot a matrix of Pareto front panels | plotObjectivePairs |
| A classical 2-dimensional plot of Pareto fronts | plotParetoFronts2D |
| Predefined precalculation functions for objectives | crossValidation precalculation reclassification |
| TunePareto wrappers for certain classifiers | predefinedClassifiers tunePareto.knn tunePareto.NaiveBayes tunePareto.randomForest tunePareto.svm tunePareto.tree |
| Predefined objective functions for parameter tuning | cvAccuracy 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 objects | predict.TuneParetoModel |
| Print method for objects used in TunePareto | print.TuneParetoClassifier print.TuneParetoModel print.TuneParetoResult |
| Rank results according to their desirabilities | rankByDesirability |
| Recalculate Pareto-optimal solutions | recalculateParetoSet |
| Train a TunePareto classifier | trainTuneParetoClassifier |
| Generic function for multi-objective parameter tuning of classifiers | tunePareto |
| Create a classifier object | tuneParetoClassifier |
