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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 931–940 of 2050 papers

TitleStatusHype
Fast Linear Model Trees by PILOT—0
SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era—0
Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomes—0
Improving Group Lasso for high-dimensional categorical data—0
Fast leave-one-cluster-out cross-validation using clustered Network Information Criterion (NICc)—0
Improving Model Robustness Using Causal Knowledge—0
Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations—0
A Regret-Variance Trade-Off in Online Learning—0
Fast approximations of the Jeffreys divergence between univariate Gaussian mixture models via exponential polynomial densities—0
Carbon Intensity-Aware Adaptive Inference of DNNs—0
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