cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R
"Optimal cutpoints" for binary classification tasks are often established by testing which cutpoint yields the best discrimination, for example the Youden index, in a specific sample. This results in "optimal" cutpoints that are highly variable and systematically overestimate the...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2021-06-01
|
| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/3429 |
| _version_ | 1827935812169236480 |
|---|---|
| author | Christian Thiele Gerrit Hirschfeld |
| author_facet | Christian Thiele Gerrit Hirschfeld |
| author_sort | Christian Thiele |
| collection | DOAJ |
| description | "Optimal cutpoints" for binary classification tasks are often established by testing which cutpoint yields the best discrimination, for example the Youden index, in a specific sample. This results in "optimal" cutpoints that are highly variable and systematically overestimate the out-of-sample performance. To address these concerns, the cutpointr package offers robust methods for estimating optimal cutpoints and the out-of-sample performance. The robust methods include bootstrapping and smoothing based on kernel estimation, generalized additive models, smoothing splines, and local regression. These methods can be applied to a wide range of binary-classification and cost-based metrics. cutpointr also provides mechanisms to utilize user-defined metrics and estimation methods. The package has capabilities for parallelization of the bootstrapping, including reproducible random number generation. Furthermore, it is pipe-friendly, for example for compatibility with functions from tidyverse. Various functions for plotting receiver operating characteristic curves, precision recall graphs, bootstrap results and other representations of the data are included. The package contains example data from a study on psychological characteristics and suicide attempts suitable for applying binary classification algorithms. |
| first_indexed | 2024-03-13T08:00:14Z |
| format | Article |
| id | doaj.art-854db438548c4d61a89dbb967a1c05f9 |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-03-13T08:00:14Z |
| publishDate | 2021-06-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-854db438548c4d61a89dbb967a1c05f92023-06-01T18:41:07ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602021-06-0198110.18637/jss.v098.i113285cutpointr: Improved Estimation and Validation of Optimal Cutpoints in RChristian ThieleGerrit Hirschfeld"Optimal cutpoints" for binary classification tasks are often established by testing which cutpoint yields the best discrimination, for example the Youden index, in a specific sample. This results in "optimal" cutpoints that are highly variable and systematically overestimate the out-of-sample performance. To address these concerns, the cutpointr package offers robust methods for estimating optimal cutpoints and the out-of-sample performance. The robust methods include bootstrapping and smoothing based on kernel estimation, generalized additive models, smoothing splines, and local regression. These methods can be applied to a wide range of binary-classification and cost-based metrics. cutpointr also provides mechanisms to utilize user-defined metrics and estimation methods. The package has capabilities for parallelization of the bootstrapping, including reproducible random number generation. Furthermore, it is pipe-friendly, for example for compatibility with functions from tidyverse. Various functions for plotting receiver operating characteristic curves, precision recall graphs, bootstrap results and other representations of the data are included. The package contains example data from a study on psychological characteristics and suicide attempts suitable for applying binary classification algorithms.https://www.jstatsoft.org/index.php/jss/article/view/3429optimal cutpointROC curvebootstrapR |
| spellingShingle | Christian Thiele Gerrit Hirschfeld cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R Journal of Statistical Software optimal cutpoint ROC curve bootstrap R |
| title | cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R |
| title_full | cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R |
| title_fullStr | cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R |
| title_full_unstemmed | cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R |
| title_short | cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R |
| title_sort | cutpointr improved estimation and validation of optimal cutpoints in r |
| topic | optimal cutpoint ROC curve bootstrap R |
| url | https://www.jstatsoft.org/index.php/jss/article/view/3429 |
| work_keys_str_mv | AT christianthiele cutpointrimprovedestimationandvalidationofoptimalcutpointsinr AT gerrithirschfeld cutpointrimprovedestimationandvalidationofoptimalcutpointsinr |