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Applications of python to evaluate the performance of decision tree‐based boosting algorithms
Author(s) -
Kadiyala Akhil,
Kumar Ashok
Publication year - 2018
Publication title -
environmental progress and sustainable energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.495
H-Index - 66
eISSN - 1944-7450
pISSN - 1944-7442
DOI - 10.1002/ep.12888
Subject(s) - boosting (machine learning) , machine learning , gradient boosting , decision tree , computer science , python (programming language) , algorithm , adaboost , artificial intelligence , scalability , decision tree learning , random forest , support vector machine , database , operating system
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