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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 1461–1470 of 2050 papers

TitleStatusHype
Distributed filtered hyperinterpolation for noisy data on the sphere—0
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries—0
Model Order Selection Based on Information Theoretic Criteria: Design of the Penalty—0
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion—0
Rejoinder on: Minimal penalties and the slope heuristics: a survey—0
On summarized validation curves and generalization—0
A Base Model Selection Methodology for Efficient Fine-Tuning—0
Interpretable Models for Understanding Immersive Simulations—0
The column measure and Gradient-Free Gradient Boosting—0
Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis—0
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