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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 1461–1470 of 2050 papers

TitleStatusHype
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference ModelsCode0
Deep Elastic Networks with Model Selection for Multi-Task Learning—0
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A SurveyCode0
Quality Estimation for Image Captions Based on Large-scale Human EvaluationsCode0
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological InferenceCode0
On the overestimation of widely applicable Bayesian information criterion—0
Model Selection With Graphical Neighbour Information—0
EPP: interpretable score of model predictive powerCode0
Parkinson's Disease Recognition Using SPECT Image and Interpretable AI: A Tutorial—0
Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms—0
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