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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 1681–1690 of 2050 papers

TitleStatusHype
Structured Variational Learning of Bayesian Neural Networks with Horseshoe PriorsCode0
Fairness and bias correction in machine learning for depression prediction: results from four study populationsCode0
Model-based Clustering using Automatic Differentiation: Confronting Misspecification and High-Dimensional DataCode0
Familia: An Open-Source Toolkit for Industrial Topic ModelingCode0
Entity Set Search of Scientific Literature: An Unsupervised Ranking ApproachCode0
Fast and Informative Model Selection using Learning Curve Cross-ValidationCode0
Automatic Catalog of RRLyrae from 14 million VVV Light Curves: How far can we go with traditional machine-learning?Code0
Adaptive multi-penalty regularization based on a generalized Lasso pathCode0
Model Evaluation, Model Selection, and Algorithm Selection in Machine LearningCode0
Fast Cross-Validation via Sequential TestingCode0
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