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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 2001–2010 of 2050 papers

TitleStatusHype
Leveraging Predictive Equivalence in Decision TreesCode0
Variational Resampling Based Assessment of Deep Neural Networks under Distribution ShiftCode0
On the cross-validation bias due to unsupervised pre-processingCode0
DECODE: Domain-aware Continual Domain Expansion for Motion PredictionCode0
Data-driven discovery of PDEs in complex datasetsCode0
Spatio-temporal Bayesian On-line Changepoint Detection with Model SelectionCode0
The Topology and Geometry of Neural RepresentationsCode0
Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method GenerationCode0
Approximate Cross-validation: Guarantees for Model Assessment and SelectionCode0
Spectral clustering on spherical coordinates under the degree-corrected stochastic blockmodelCode0
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