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• W2897833335 abstract "This study compares the performance of ensemble machine learning, ordinary least-squared and penalized algorithms to predict taxi-out time at two different periods of NextGen capability implementation. In the pre-sample, ordinary least-squared and ridge models performed better than other ensemble learning models. However, the gradient boosting model provided the lowest root mean squared errors in the post-sample. No algorithm fits data better in all cases. This paper recommends selecting the model that provides the best balance between bias and variance." @default.
• W2897833335 created "2018-10-26" @default.
• W2897833335 creator A5007925451 @default.
• W2897833335 date "2018-11-01" @default.
• W2897833335 modified "2023-10-17" @default.
• W2897833335 title "Can machines learn how to forecast taxi-out time? A comparison of predictive models applied to the case of Seattle/Tacoma International Airport" @default.
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• W2897833335 doi "https://doi.org/10.1016/j.tre.2018.10.003" @default.
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