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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 751–760 of 2050 papers

TitleStatusHype
Fitting very flexible models: Linear regression with large numbers of parameters—0
Face Recognition using Optimal Representation Ensemble—0
Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias—0
Factor-Augmented Regularized Model for Hazard Regression—0
Can Pre-training Indicators Reliably Predict Fine-tuning Outcomes of LLMs?—0
Factorized Asymptotic Bayesian Inference for Factorial Hidden Markov Models—0
Forecasting large collections of time series: feature-based methods—0
Factors in Fashion: Factor Analysis towards the Mode—0
Can We Use Gradient Norm as a Measure of Generalization Error for Model Selection in Practice?—0
Foundation of Calculating Normalized Maximum Likelihood for Continuous Probability Models—0
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