Provides a finite-sample minimax characterization of black-box assisted regression with a phase transition at δ_c(n) ~ n^{-β/(2β+d)} and a safe residual estimator achieving near-optimal risk.
A survey of cross-validation procedures for model selection , volume =
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Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.
A framework for cross-validation optimal feature selection in linear SVM classification is developed by reformulating the bilevel problem into a single-level mixed-integer optimization using LS-SVM, with simulation results indicating competitive performance.
Improper use of test data during hyperparameter tuning in link prediction inflates performance estimates by an average of 3.6 percent across 60 networks, as measured by a new Loss Ratio metric.
MDL and BIC most reliably select low test-error models and recover ground-truth expressions in symbolic regression benchmarks.
Augmenting zone-level MTPL claim frequency models with coordinates, environmental features at 5 km scale, and image embeddings improves predictive accuracy on unseen postcodes across GLM, regularized GLM, and tree-based models.
Spectra-Scope is a new AutoML framework that trains interpretable machine learning models on spectral data to characterize material properties while enabling users to understand which spectral features drive the predictions.
Model evaluation in supervised learning should be treated as a context-dependent, decision-oriented process aligned with operational objectives rather than relying on a small set of aggregate metrics.
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Cross-validation-based optimal feature selection for linear SVM classification
A framework for cross-validation optimal feature selection in linear SVM classification is developed by reformulating the bilevel problem into a single-level mixed-integer optimization using LS-SVM, with simulation results indicating competitive performance.