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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 561–570 of 2050 papers

TitleStatusHype
Bridging the Bosphorus: Advancing Turkish Large Language Models through Strategies for Low-Resource Language Adaptation and Benchmarking—0
Don't Waste Your Time: Early Stopping Cross-ValidationCode0
On Quantum Ambiguity and Potential Exponential Computational Speed-Ups to Solving Dynamic Asset Pricing Models—0
Misclassification bounds for PAC-Bayesian sparse deep learning—0
KITE: A Kernel-based Improved Transferability Estimation Method—0
MRScore: Evaluating Radiology Report Generation with LLM-based Reward System—0
Automated Model Selection for Generalized Linear Models—0
mlr3summary: Concise and interpretable summaries for machine learning modelsCode0
Estimating the Number of Components in Finite Mixture Models via Variational Approximation—0
On uncertainty-penalized Bayesian information criterion—0
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