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:
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
- aeAegypti - Males and Females Aedes Aegypti data from Maletzke
Last updated from:5d7c9ea9ca. Checks:7 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | NOTE | 126 | ||
| source / vignettes | OK | 172 | ||
| linux-release | NOTE | 126 | ||
| macos-release | NOTE | 156 | ||
| macos-oldrel | NOTE | 169 | ||
| windows-devel | NOTE | 84 | ||
| windows-release | NOTE | 96 | ||
| windows-oldrel | NOTE | 86 | ||
| wasm-release | OK | 103 |
Exports:ACCCCDySEMQgetTPRandFPRbyThresholdHDy_LPKUIPERMAXMKSMSMS2PACCPCCPWKSMMSORDT50X
Dependencies:caretclasscliclockcodetoolscpp11data.tablediagramdigestdplyre1071farverFNNforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixModelMetricsnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6randomForestRColorBrewerRcpprecipesreshape2rlangrpartS7scalesshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr
