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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 801–810 of 2050 papers

TitleStatusHype
Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint AveragingCode0
Accurate generation of stochastic dynamics based on multi-model Generative Adversarial Networks—0
Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID—0
Learning Relevant Contextual Variables Within Bayesian OptimizationCode0
Clustering Indices based Automatic Classification Model SelectionCode0
Unraveling Cold Start Enigmas in Predictive Analytics for OTT Media: Synergistic Meta-Insights and Multimodal Ensemble Mastery—0
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model—0
Ranking & Reweighting Improves Group Distributional Robustness—0
Boldness-Recalibration for Binary Event Predictions—0
fairml: A Statistician's Take on Fair Machine Learning Modelling—0
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