Package: mlquantify 0.2.0

mlquantify: Algorithms for Class Distribution Estimation

Quantification is a prominent machine learning task that has received an increasing amount of attention in the last years. The objective is to predict the class distribution of a data sample. This package is a collection of machine learning algorithms for class distribution estimation. This package include algorithms from different paradigms of quantification. These methods are described in the paper: A. Maletzke, W. Hassan, D. dos Reis, and G. Batista. The importance of the test set size in quantification assessment. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI20, pages 2640–2646, 2020. <doi:10.24963/ijcai.2020/366>.

Authors:Andre Maletzke [aut, cre], Everton Cherman [ctb], Denis dos Reis [ctb], Gustavo Batista [ths]

mlquantify_0.2.0.tar.gz
mlquantify_0.2.0.zip(r-4.7-any)mlquantify_0.2.0.zip(r-4.6-any)mlquantify_0.2.0.zip(r-4.5-any)
mlquantify_0.2.0.tgz(r-4.6-any)mlquantify_0.2.0.tgz(r-4.5-any)
mlquantify_0.2.0.tar.gz(r-4.7-any)mlquantify_0.2.0.tar.gz(r-4.6-any)
mlquantify_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
mlquantify/json (API)

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

Bug tracker:https://github.com/andregustavom/mlquantify/issues

Datasets:
  • aeAegypti - Males and Females Aedes Aegypti data from Maletzke

On CRAN:

Conda:

3.54 score 7 stars 1 scripts 271 downloads 18 exports 75 dependencies

Last updated from:5d7c9ea9ca. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develNOTE126
source / vignettesOK172
linux-releaseNOTE126
macos-releaseNOTE156
macos-oldrelNOTE169
windows-develNOTE84
windows-releaseNOTE96
windows-oldrelNOTE86
wasm-releaseOK103

Exports:ACCCCDySEMQgetTPRandFPRbyThresholdHDy_LPKUIPERMAXMKSMSMS2PACCPCCPWKSMMSORDT50X

Dependencies:caretclasscliclockcodetoolscpp11data.tablediagramdigestdplyre1071farverFNNforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixModelMetricsnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6randomForestRColorBrewerRcpprecipesreshape2rlangrpartS7scalesshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr