chemmodlab: a cheminformatics modeling laboratory R package for fitting and assessing machine learning models
Abstract The goal of chemmodlab is to streamline the fitting and assessment pipeline for many machine learning models in R, making it easy for researchers to compare the utility of these models. While focused on implementing methods for model fitting and assessment that have been accepted by experts...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2018-11-01
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| Series: | Journal of Cheminformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13321-018-0309-4 |
| _version_ | 1818130944891027456 |
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| author | Jeremy R. Ash Jacqueline M. Hughes-Oliver |
| author_facet | Jeremy R. Ash Jacqueline M. Hughes-Oliver |
| author_sort | Jeremy R. Ash |
| collection | DOAJ |
| description | Abstract The goal of chemmodlab is to streamline the fitting and assessment pipeline for many machine learning models in R, making it easy for researchers to compare the utility of these models. While focused on implementing methods for model fitting and assessment that have been accepted by experts in the cheminformatics field, all of the methods in chemmodlab have broad utility for the machine learning community. chemmodlab contains several assessment utilities, including a plotting function that constructs accumulation curves and a function that computes many performance measures. The most novel feature of chemmodlab is the ease with which statistically significant performance differences for many machine learning models is presented by means of the multiple comparisons similarity plot. Differences are assessed using repeated k-fold cross validation, where blocking increases precision and multiplicity adjustments are applied. chemmodlab is freely available on CRAN at https://cran.r-project.org/web/packages/chemmodlab/index.html. |
| first_indexed | 2024-12-11T08:13:05Z |
| format | Article |
| id | doaj.art-38c09a78e6fe4546a8fba2acfe5d2a5e |
| institution | Directory Open Access Journal |
| issn | 1758-2946 |
| language | English |
| last_indexed | 2024-12-11T08:13:05Z |
| publishDate | 2018-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj.art-38c09a78e6fe4546a8fba2acfe5d2a5e2022-12-22T01:14:50ZengBMCJournal of Cheminformatics1758-29462018-11-0110112010.1186/s13321-018-0309-4chemmodlab: a cheminformatics modeling laboratory R package for fitting and assessing machine learning modelsJeremy R. Ash0Jacqueline M. Hughes-Oliver1Department of Statistics, Bioinformatics Research Center, North Carolina State UniversityDepartment of Statistics, North Carolina State UniversityAbstract The goal of chemmodlab is to streamline the fitting and assessment pipeline for many machine learning models in R, making it easy for researchers to compare the utility of these models. While focused on implementing methods for model fitting and assessment that have been accepted by experts in the cheminformatics field, all of the methods in chemmodlab have broad utility for the machine learning community. chemmodlab contains several assessment utilities, including a plotting function that constructs accumulation curves and a function that computes many performance measures. The most novel feature of chemmodlab is the ease with which statistically significant performance differences for many machine learning models is presented by means of the multiple comparisons similarity plot. Differences are assessed using repeated k-fold cross validation, where blocking increases precision and multiplicity adjustments are applied. chemmodlab is freely available on CRAN at https://cran.r-project.org/web/packages/chemmodlab/index.html.http://link.springer.com/article/10.1186/s13321-018-0309-4Machine learningQSARR packageInitial enhancementEnrichment factorAccumulation curve |
| spellingShingle | Jeremy R. Ash Jacqueline M. Hughes-Oliver chemmodlab: a cheminformatics modeling laboratory R package for fitting and assessing machine learning models Journal of Cheminformatics Machine learning QSAR R package Initial enhancement Enrichment factor Accumulation curve |
| title | chemmodlab: a cheminformatics modeling laboratory R package for fitting and assessing machine learning models |
| title_full | chemmodlab: a cheminformatics modeling laboratory R package for fitting and assessing machine learning models |
| title_fullStr | chemmodlab: a cheminformatics modeling laboratory R package for fitting and assessing machine learning models |
| title_full_unstemmed | chemmodlab: a cheminformatics modeling laboratory R package for fitting and assessing machine learning models |
| title_short | chemmodlab: a cheminformatics modeling laboratory R package for fitting and assessing machine learning models |
| title_sort | chemmodlab a cheminformatics modeling laboratory r package for fitting and assessing machine learning models |
| topic | Machine learning QSAR R package Initial enhancement Enrichment factor Accumulation curve |
| url | http://link.springer.com/article/10.1186/s13321-018-0309-4 |
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