ENTERPRISE AI RESEARCH ANALYSIS
Calibrated and Explainable Flight Delay Prediction with Tree-Based Models in Machine Learning
This study leverages tree-based machine learning models to predict flight delays at Boston Logan Airport (BOS), focusing on calibrated probabilities and explainable drivers. By integrating flight, weather, and calendar data, the research achieves high predictive accuracy and provides actionable insights for operational decision-making, significantly enhancing proactive delay management.
Executive Impact at a Glance
Proactive flight delay prediction and management can translate directly into substantial operational efficiencies and improved customer satisfaction.
0.939
XGBoost ROC-AUC
0.884
XGBoost F1-Score
0.087
Calibrated Brier Score
17.6B
Potential U.S. Net Welfare (10% Delay Reduction)
Deep Analysis & Enterprise Applications
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