Model population analysis in model evaluation

Model evaluation plays a central role in chemical modeling. Model population analysis (MPA), a general framework for designing new types of chemometrics algorithms, has shown its advantage in the field of model evaluation. The core idea of MPA is to statistically analyze the outputs of randomly gene...

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Published inChemometrics and intelligent laboratory systems Vol. 172; pp. 223 - 228
Main Authors Deng, Baichuan, Lu, Hongmei, Tan, Chengquan, Deng, Jinping, Yin, Yulong
Format Journal Article
LanguageEnglish
Published Elsevier B.V 15.01.2018
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ISSN0169-7439
1873-3239
DOI10.1016/j.chemolab.2017.11.016

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Abstract Model evaluation plays a central role in chemical modeling. Model population analysis (MPA), a general framework for designing new types of chemometrics algorithms, has shown its advantage in the field of model evaluation. The core idea of MPA is to statistically analyze the outputs of randomly generated sub-models to extract interesting information from the data. One of the most obvious characteristics of MPA-based methods is that they use multiple models instead of a single model for model evaluation. In this review, we described the concept of MPA, and then discussed the application of MPA in model evaluation, including the relationship between MPA and cross-validation, model comparison, randomization tests, model stability, variable importance and sum of rank differences. Finally, we prospected the potential application of MPA in model evaluation. •Model population analysis (MPA) is a general framework for chemometrics algorithms.•We reviewed the development of MPA in model evaluation.•We prospect the potential application of MPA in model evaluation.
AbstractList Model evaluation plays a central role in chemical modeling. Model population analysis (MPA), a general framework for designing new types of chemometrics algorithms, has shown its advantage in the field of model evaluation. The core idea of MPA is to statistically analyze the outputs of randomly generated sub-models to extract interesting information from the data. One of the most obvious characteristics of MPA-based methods is that they use multiple models instead of a single model for model evaluation. In this review, we described the concept of MPA, and then discussed the application of MPA in model evaluation, including the relationship between MPA and cross-validation, model comparison, randomization tests, model stability, variable importance and sum of rank differences. Finally, we prospected the potential application of MPA in model evaluation. •Model population analysis (MPA) is a general framework for chemometrics algorithms.•We reviewed the development of MPA in model evaluation.•We prospect the potential application of MPA in model evaluation.
Author Deng, Jinping
Lu, Hongmei
Deng, Baichuan
Yin, Yulong
Tan, Chengquan
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  organization: College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China
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  givenname: Jinping
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  fullname: Deng, Jinping
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  email: yinyulong@isa.ac.cn
  organization: Guangdong Provincial Key Laboratory of Animal Nutrition Control, Institute of Subtropical Animal Nutrition and Feed, College of Animal Science, South China Agricultural University, Guangzhou 510642, PR China
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Keywords Model evaluation
Model comparison
Model population analysis
Model selection
Language English
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Model population analysis
Model selection
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