For any function computable by an optimal decision tree with size s, max depth D_opt and average depth Δ_opt, the greedy heuristic builds an ε-approximating tree of size at most exp(Δ_opt D_opt log(e/ε)) under arbitrary product distributions.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.
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Decision Tree Learning on Product Spaces
For any function computable by an optimal decision tree with size s, max depth D_opt and average depth Δ_opt, the greedy heuristic builds an ε-approximating tree of size at most exp(Δ_opt D_opt log(e/ε)) under arbitrary product distributions.
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Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework
Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.