Auto-WEKA combined learner and hyperparameter selection

What changed

Encodes algorithm choice as a root conditional hyperparameter and jointly searches learner-specific settings using sequential model-based optimization in WEKA.

Scientific scope

Encodes algorithm choice as a root conditional hyperparameter and jointly searches learner-specific settings using sequential model-based optimization in WEKA. The controlling2012technical report differs from the later2013publication in title/counts; its47classifier inventory is not mixed with later39counts. SMAC/TPE/cross-validation are inherited. Finite-budget empirical comparisons do not guarantee a globally best classifier.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. The controlling2012technical report differs from the later2013publication in title/counts; its47classifier inventory is not mixed with later39counts. SMAC/TPE/cross-validation are inherited. Finite-budget empirical comparisons do not guarantee a globally best classifier.

Contributors

• Source-listed authors and technical contributors · SCIENCE: 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.

• Auto-WEKA: Automated Selection and Hyper-Parameter Optimization of Classification Algorithms

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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