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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 1241–1250 of 2050 papers

TitleStatusHype
Surveying Off-Board and Extra-Vehicular Monitoring and Progress Towards Pervasive Diagnostics—0
Synthetic Data for Model Selection—0
Systematic Ensemble Model Selection Approach for Educational Data Mining—0
Understanding Best Subset Selection: A Tale of Two C(omplex)ities—0
Taming Nonconvexity in Kernel Feature Selection -- Favorable Properties of the Laplace Kernel—0
Target Variable Engineering—0
Task-Distributionally Robust Data-Free Meta-Learning—0
Techniques for clustering interaction data as a collection of graphs—0
Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work—0
Temporal Answer Set Programming—0
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