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mlquantify  

Algorithms for Class Distribution Estimation
View on CRAN: Click here


Download and install mlquantify package within the R console
Install from CRAN:
install.packages("mlquantify")

Install from Github:
library("remotes")
install_github("cran/mlquantify")

Install by package version:
library("remotes")
install_version("mlquantify", "0.2.0")



Attach the package and use:
library("mlquantify")
Maintained by
Andre Maletzke
[Scholar Profile | Author Map]
First Published: 2020-09-23
Latest Update: 2022-01-20
Description:
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
How to cite:
Andre Maletzke (2020). mlquantify: Algorithms for Class Distribution Estimation. R package version 0.2.0, https://cran.r-project.org/web/packages/mlquantify. Accessed 05 May. 2025.
Previous versions and publish date:
0.1.4 (2020-09-23 11:20), 0.1.5 (2021-04-13 17:20)
Other packages that cited mlquantify R package
View mlquantify citation profile
Other R packages that mlquantify depends, imports, suggests or enhances
Complete documentation for mlquantify
Functions, R codes and Examples using the mlquantify R package
Some associated functions: ACC . CC . DyS . EMQ . HDy_LP . KUIPER . MAX . MKS . MS . MS2 . PACC . PCC . PWK . SMM . SORD . T50 . X . aeAegypti . getTPRandFPRbyThreshold . 
Some associated R codes: Full mlquantify package functions and examples
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