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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 411–420 of 2050 papers

TitleStatusHype
Clinical prediction system of complications among COVID-19 patients: a development and validation retrospective multicentre studyCode0
A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective LandscapesCode0
An Offline Metric for the Debiasedness of Click ModelsCode0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
Execution-based Evaluation for Data Science Code Generation ModelsCode0
Face Spoofing Detection using Deep LearningCode0
IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmarkCode0
Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method GenerationCode0
Clustering Indices based Automatic Classification Model SelectionCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
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