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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 921–930 of 2050 papers

TitleStatusHype
ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets—0
Fast rates with high probability in exp-concave statistical learning—0
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods—0
Learning stable and predictive structures in kinetic systems: Benefits of a causal approach—0
Identifying spatiotemporal dynamics of Ebola in Sierra Leone using virus genomes—0
Identifying Technical Debt and Its Types Across Diverse Software Projects Issues—0
AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow—0
Fast model selection by limiting SVM training times—0
Fast Linear Model Trees by PILOT—0
SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era—0
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