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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 1391–1400 of 2050 papers

TitleStatusHype
Weighted Sampling for Combined Model Selection and Hyperparameter Tuning—0
What are the mechanisms underlying metacognitive learning?—0
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution—0
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development—0
When Is the First Spurious Variable Selected by Sequential Regression Procedures?—0
Which is the best model for my data?—0
Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits—0
Why risk matters for protein binder design—0
Within-vector viral dynamics challenges how to model the extrinsic incubation period for major arboviruses: dengue, Zika, and chikungunya—0
Z-AGI Labs at ClimateActivism 2024: Stance and Hate Event Detection on Social Media—0
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