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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 1121–1130 of 2050 papers

TitleStatusHype
Vector autoregression models with skewness and heavy tails—0
Informative Bayesian model selection for RR Lyrae star classifiers—0
True Few-Shot Learning with Language ModelsCode1
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science DomainsCode1
Hypothesis Testing for Equality of Latent Positions in Random Graphs—0
Laplace Redux - Effortless Bayesian Deep Learning—0
Towards Model Selection using Learning Curve Cross-ValidationCode0
On-the-fly learning of adaptive strategies with bandit algorithms—0
Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy—0
Achieving Fairness with a Simple Ridge Penalty—0
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