Applications of python to evaluate the performance of decision tree‐based boosting algorithms
The scope for identifying scalable solutions through the application of machine learning algorithms on diverse datasets has increased manifold with rapid advancements in the field of computer science and technology. This article demonstrates a step‐by‐step approach to the applications of python to e...
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| Published in | Environmental progress & sustainable energy Vol. 37; no. 2; pp. 618 - 623 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
01.03.2018
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1944-7442 1944-7450 |
| DOI | 10.1002/ep.12888 |
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| Abstract | The scope for identifying scalable solutions through the application of machine learning algorithms on diverse datasets has increased manifold with rapid advancements in the field of computer science and technology. This article demonstrates a step‐by‐step approach to the applications of python to evaluate the performance of decision tree‐based gradient boosting machine (gbm), lightgbm, extreme gradient boosting (xgboost), and adaptive boosting (adaboost) algorithms for predicting the in‐bus carbon dioxide concentrations. Among the four boosting algorithms examined in this study, the xgboost algorithm provided better results on the basis of predictive model evaluation with operational performance measures. The readers may adopt the methods (inclusive of the python coding) discussed in this article to successfully address their own data science problems. © 2017 American Institute of Chemical Engineers Environ Prog, 37: 618–623, 2018 |
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| AbstractList | The scope for identifying scalable solutions through the application of machine learning algorithms on diverse datasets has increased manifold with rapid advancements in the field of computer science and technology. This article demonstrates a step‐by‐step approach to the applications of python to evaluate the performance of decision tree‐based gradient boosting machine (gbm), lightgbm, extreme gradient boosting (xgboost), and adaptive boosting (adaboost) algorithms for predicting the in‐bus carbon dioxide concentrations. Among the four boosting algorithms examined in this study, the xgboost algorithm provided better results on the basis of predictive model evaluation with operational performance measures. The readers may adopt the methods (inclusive of the python coding) discussed in this article to successfully address their own data science problems. © 2017 American Institute of Chemical Engineers Environ Prog, 37: 618–623, 2018 |
| Author | Kadiyala, Akhil Kumar, Ashok |
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| Cites_doi | 10.2174/1875040001104010001 10.1007/s11869-011-0163-2 10.1002/ep.12021 10.1002/ep.10292 10.1002/ep.11708 10.1002/ep.12676 10.1002/ep.12523 10.3390/atmos3020266 10.1080/10962247.2012.741054 10.2174/1874829501003010055 10.1002/ep.12387 10.1016/j.jhazmat.2012.01.072 10.1002/ep.12119 10.1002/ep.12349 10.1002/ep.12273 10.1002/ep.11959 10.1002/ep.10527 10.1002/ep.11642 10.1002/ep.12786 10.1002/ep.12199 |
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| SubjectTerms | adaboost anaconda biodiesel data science gbm indoor air quality lightgbm numpy public transportation buses python scikit‐learn spyder xgboost |
| Title | Applications of python to evaluate the performance of decision tree‐based boosting algorithms |
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