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 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.