This thesis aims to show that a Stacking Ensemble of multiple base-learners can provide a more accurate prediction of commercial flight delays between the ten largest US airports than the individual prediction models. Three types of machine learning models, namely LASSO, Random Forests and Neural Networks are used as base-learners with different hyper- parameters. A Stacking Ensemble is created by using LASSO as meta-learner. The Stacking Ensemble and the base-learners that performed best on the training data are then evaluated on a test data set. The results are compared by the metrics accuracy, ROC AUC, MCC and F1 Score. It is shown that the Stacking Ensemble is able to provide superior predictions for flight delays in comparison to the best individual models.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-489493 |
| Date | January 2022 |
| Creators | Schwarz, Patrick |
| Publisher | Uppsala universitet, Statistiska institutionen |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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