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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 581–590 of 2050 papers

TitleStatusHype
Bayesian Joint Spike-and-Slab Graphical LassoCode0
Graphical posterior predictive classifier: Bayesian model averaging with particle GibbsCode0
An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced ClassificationCode0
DiffPrune: Neural Network Pruning with Deterministic Approximate Binary Gates and L_0 RegularizationCode0
Hierarchical clustering: visualization, feature importance and model selectionCode0
High-dimensional classification by sparse logistic regressionCode0
Hybrid safe-strong rules for efficient optimization in lasso-type problemsCode0
Embarrassingly Simple Performance Prediction for Abductive Natural Language InferenceCode0
Differentiable Model Selection for Ensemble LearningCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
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