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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 241–250 of 2050 papers

TitleStatusHype
Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence ModelingCode0
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-LearningCode0
Fairness and bias correction in machine learning for depression prediction: results from four study populationsCode0
All models are wrong, some are useful: Model Selection with Limited LabelsCode0
A multiple testing framework for diagnostic accuracy studies with co-primary endpointsCode0
Automated Model Selection for Tabular DataCode0
Optimal design of experiments to identify latent behavioral typesCode0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
Automatic AI Model Selection for Wireless Systems: Online Learning via Digital TwinningCode0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
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