TabArena launches a dynamic, updatable benchmarking system for tabular ML that shows boosted trees remain competitive, deep learning matches them under larger budgets with ensembling, foundation models excel on small data, and cross-model ensembles advance SOTA while flagging validation overfitting.
Sparse spatial autoregressions.Statistics & Probability Letters, 33(3):291–297
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ShapShift explains prediction shifts by attributing them to changes in conditional probabilities of tree-defined subgroups via conditional Shapley values, with exact computation for single trees and surrogate extensions for other models.
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TabArena: A Living Benchmark for Machine Learning on Tabular Data
TabArena launches a dynamic, updatable benchmarking system for tabular ML that shows boosted trees remain competitive, deep learning matches them under larger budgets with ensembling, foundation models excel on small data, and cross-model ensembles advance SOTA while flagging validation overfitting.
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ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Values
ShapShift explains prediction shifts by attributing them to changes in conditional probabilities of tree-defined subgroups via conditional Shapley values, with exact computation for single trees and surrogate extensions for other models.