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hmeasure  

The H-Measure and Other Scalar Classification Performance Metrics
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("hmeasure", "1.0-2")



Attach the package and use:
library("hmeasure")
Maintained by
Christoforos Anagnostopoulos
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2012-09-10
Latest Update: 2019-02-26
Description:
Classification performance metrics that are derived from the ROC curve of a classifier. The package includes the H-measure performance metric as described in , which computes the minimum total misclassification cost, integrating over any uncertainty about the relative misclassification costs, as per a user-defined prior. It also offers a one-stop-shop for other scalar metrics of performance, including sensitivity, specificity and many others, and also offers plotting tools for ROC curves and related statistics.
How to cite:
Christoforos Anagnostopoulos (2012). hmeasure: The H-Measure and Other Scalar Classification Performance Metrics. R package version 1.0-2, https://cran.r-project.org/web/packages/hmeasure. Accessed 06 Jan. 2025.
Previous versions and publish date:
1.0-1 (2019-01-02 09:40), 1.0 (2012-09-10 13:50)
Other packages that cited hmeasure R package
View hmeasure citation profile
Other R packages that hmeasure depends, imports, suggests or enhances
Complete documentation for hmeasure
Functions, R codes and Examples using the hmeasure R package
Some associated functions: HMeasure . hmeasure-package . misclassCounts . plotROC . relabel . summary.hmeasure . 
Some associated R codes: library_aux.R . library_metrics.R . library_plotting.R .  Full hmeasure package functions and examples
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