What is recorded here?
Builds heterogeneous multilayer stack ensembles using original features and out-of-fold model predictions, repeated bagging and weighted ensemble selection for tabular learning.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Bagging, stacking, base learners and ensemble selection are inherited. The source notes early-stopping-related leakage can subtly overfit out-of-fold predictions; empirical benchmark/competition comparisons do not guarantee universal superiority. First two authors contribute equally.
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People and systems
Nick Erickson; Jonas Mueller; Alexander Shirkov; Hang Zhang; Pedro Larroy; Mu Li; Alexander Smola
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
AutoGluon-Tabular multilayer ensemble AutoML
Builds heterogeneous multilayer stack ensembles using original features and out-of-fold model predictions, repeated bagging and weighted ensemble selection for tabular learning.
Scientific scope
Bagging, stacking, base learners and ensemble selection are inherited. The source notes early-stopping-related leakage can subtly overfit out-of-fold predictions; empirical benchmark/competition comparisons do not guarantee universal superiority. First two authors contribute equally.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Nick Erickson; Jonas Mueller; Alexander Shirkov; Hang Zhang; Pedro Larroy; Mu Li; Alexander Smola
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.