Package: ClassificationEnsembles 1.1.0
ClassificationEnsembles: Automated Multi-Class Stacking Ensembles and Predictive Diagnostics
Automated multi-class classification ensemble pipeline executing concurrent parameter optimizations across logistic, tree, kernel, and neural configurations. Implements dynamic out-of-sample blending, automated feature reduction via iterative Variance Inflation Factors (VIF), and native S3 diagnostic dashboards.
Authors:
ClassificationEnsembles_1.1.0.tar.gz
ClassificationEnsembles_1.1.0.zip(r-4.7)ClassificationEnsembles_1.1.0.zip(r-4.6)ClassificationEnsembles_1.1.0.zip(r-4.5)
ClassificationEnsembles_1.1.0.tgz(r-4.6-any)ClassificationEnsembles_1.1.0.tgz(r-4.5-any)
ClassificationEnsembles_1.1.0.tar.gz(r-4.7-any)ClassificationEnsembles_1.1.0.tar.gz(r-4.6-any)
ClassificationEnsembles_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ClassificationEnsembles/json (API)
| # Install 'ClassificationEnsembles' in R: |
| install.packages('ClassificationEnsembles', repos = c('https://infinitecuriosity.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/infintecuriosity/classificationensembles/issues
- Carseats - Carseats data
- Cleveland_heart - Cleveland Heart
- Dry_Beans - Dry Beans
- Maternal_Health_Risk - Maternal Health Risk
Last updated from:5810a92a55. Checks:7 WARNING, 1 ERROR, 1 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | WARNING | 201 | ||
| source / vignettes | ERROR | 253 | ||
| linux-release-x86_64 | WARNING | 198 | ||
| macos-release-arm64 | WARNING | 112 | ||
| macos-oldrel-arm64 | WARNING | 101 | ||
| windows-devel | WARNING | 110 | ||
| windows-release | WARNING | 134 | ||
| windows-oldrel | WARNING | 130 | ||
| wasm-release | OK | 189 |
Exports:ClassificationClassificationEnsemblesConfigClassificationEnsemblesDemoClassificationEnsemblesFastConfigload_pipelinepredict_productionsave_pipeline
Dependencies:abindbackportsbase64encbootbroombslibcachemcarcarDatacaretclasscliclockcodetoolscolorspacecommonmarkcowplotcpp11data.tableDerivdiagramdigestdoBydoParalleldplyre1071farverfastmapfontawesomeforeachforecastFormulafracdifffsfuturefuture.applygenericsggplot2ggrepelglobalsgluegowergtablehardhathtmltoolshttpuvipredisobanditeratorsjquerylibjsonliteKernSmoothlabelinglaterlatticelavalifecyclelistenvlme4lmtestlubridatemagrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqaModelMetricsmodelrnlmenloptrnnetnumDerivotelparallellypatchworkpbkrtestpillarpkgconfigplyrpROCprodlimprogressrpromisesproxypurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackrecipesreformulasreshape2rlangrpartrstudioapiS7sassscalesshapeshinysourcetoolsSparseMsparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdburcautf8vctrsviridisLitewithrxtablezoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Carseats data | Carseats |
| Comprehensive Multi-Class Classification Ensemble Pipeline Engine | Classification |
| Configuration Parameters Matrix for Classification Pipeline | ClassificationEnsemblesConfig |
| Run Fast Validation Classification Demo Suite | ClassificationEnsemblesDemo |
| Fast-Execution Configuration Matrix for Classification Verification | ClassificationEnsemblesFastConfig |
| Cleveland Heart | Cleveland_heart |
| Dry Beans | Dry_Beans |
| Load and Decompress Package Pipeline Footprints | load_pipeline |
| Maternal Health Risk | Maternal_Health_Risk |
| Plot Classification Pipeline Diagnostics Curves Canvas | plot.classification_pipeline |
| Executive Production Projections and Probabilities Matrix | predict_production |
| Predict with Classification Pipeline Object | predict.classification_pipeline |
| Print Classification Pipeline Summary Report | print.classification_pipeline |
| Compress and Save Trained Classification Pipeline Assets | save_pipeline |
