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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 1471–1480 of 2050 papers

TitleStatusHype
How have German University Tuition Fees Affected Enrollment Rates: Robust Model Selection and Design-based Inference in High-Dimensions—0
Weighted Sampling for Combined Model Selection and Hyperparameter Tuning—0
Not again! Data Leakage in Digital Pathology—0
Predictive Multiplicity in ClassificationCode0
Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization—0
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference ModelsCode0
Deep Elastic Networks with Model Selection for Multi-Task Learning—0
Quality Estimation for Image Captions Based on Large-scale Human EvaluationsCode0
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A SurveyCode0
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological InferenceCode0
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