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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 1631–1640 of 2050 papers

TitleStatusHype
Using J-K-fold Cross Validation To Reduce Variance When Tuning NLP ModelsCode0
Model selection by minimum description length: Lower-bound sample sizes for the Fisher information approximation—0
Cross Validation Based Model Selection via Generalized Method of Moments—0
Is it worth it? Budget-related evaluation metrics for model selection—0
Tune: A Research Platform for Distributed Model Selection and TrainingCode0
Optimal design of experiments to identify latent behavioral typesCode0
Automatic Gradient BoostingCode0
Pairwise Covariates-adjusted Block Model for Community Detection—0
Algebraic Equivalence of Linear Structural Equation ModelsCode0
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization—0
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