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 in | Chemometrics and intelligent laboratory systems Vol. 172; pp. 223 - 228 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
15.01.2018
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0169-7439 1873-3239 |
| DOI | 10.1016/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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Baichuan surname: Deng fullname: Deng, Baichuan 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 – sequence: 2 givenname: Hongmei surname: Lu fullname: Lu, Hongmei organization: College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China – sequence: 3 givenname: Chengquan surname: Tan fullname: Tan, Chengquan 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 – sequence: 4 givenname: Jinping surname: Deng fullname: Deng, Jinping email: dengjinping@scau.edu.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 – sequence: 5 givenname: Yulong surname: Yin fullname: Yin, Yulong 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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