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.
citing papers explorer
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Black-Box Assisted Regression: Phase Transitions and Minimax Optimality
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.
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To select or not to select: predictively consistent priors instead of model selection
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.
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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.
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Impacts of Data Splitting Strategies on Parameterized Link Prediction Algorithms
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.
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A Comparative Study of Model Selection Criteria for Symbolic Regression
MDL and BIC most reliably select low test-error models and recover ground-truth expressions in symbolic regression benchmarks.
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Revealing Geography-Driven Signals in Zone-Level Claim Frequency Models: An Empirical Study using Environmental and Visual Predictors
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.
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Spectra-Scope : A toolkit for automated and interpretable characterization of material properties from spectral data
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.
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Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection
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.