Package: nonet 0.3.0

Aviral Vijay

nonet: Weighted Average Ensemble without Training Labels

It provides ensemble capabilities to supervised and unsupervised learning models predictions without using training labels. It decides the relative weights of the different models predictions by using best models predictions as response variable and rest of the mo. User can decide the best model, therefore, It provides freedom to user to ensemble models based on their design solutions.

Authors:Aviral Vijay [aut, cre], Sameer Mahajan [aut]

nonet_0.3.0.tar.gz


nonet_0.3.0.tar.gz(r-4.4-noble)
nonet_0.3.0.tgz(r-4.4-emscripten)nonet_0.3.0.tgz(r-4.3-emscripten)
nonet.pdf |nonet.html✨
nonet/json (API)

Peer review:

Bug tracker:https://github.com/aviralvijay-gslab/nonet/issues

On CRAN:

2 exports 2 stars 0.83 score 145 dependencies 18 scripts 187 downloads

Last updated 6 years agofrom:66b416211a. Checks:OK: 1. Indexed: yes.

Exports:nonet_ensemblenonet_plot

Dependencies:askpassbackportsbase64encbitbit64blobbroombslibcachemcallrcaretcellrangerclassclicliprclockcodetoolscolorspaceconflictedcpp11crayoncurldata.tableDBIdbplyrdiagramdigestdplyrdtplyre1071evaluatefansifarverfastmapfontawesomeforcatsforeachfsfuturefuture.applygarglegenericsggplot2glmnetglobalsgluegoogledrivegooglesheets4gowergtablehardhathavenhighrhmshtmltoolshttridsipredisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmemoisemgcvmimeModelMetricsmodelrmunsellnlmennetnumDerivopensslparallellypillarpkgconfigplyrprettyunitspROCprocessxprodlimprogressprogressrproxypspurrrR6raggrandomForestrappdirsRColorBrewerRcppRcppEigenreadrreadxlrecipesrematchrematch2reprexreshape2rlangrlistrmarkdownrpartrstudioapirvestsassscalesselectrshapeSQUAREMstringistringrsurvivalsyssystemfontstextshapingtibbletidyrtidyselecttidyversetimechangetimeDatetinytextzdbutf8uuidvctrsviridisLitevroomwithrxfunXMLxml2yaml