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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 141–150 of 2050 papers

TitleStatusHype
A network approach to topic modelsCode1
A Survey and Implementation of Performance Metrics for Self-Organized MapsCode1
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet DatasetsCode1
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
Entropic Descent Archetypal Analysis for Blind Hyperspectral UnmixingCode1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD GeneralizationCode1
An information criterion for automatic gradient tree boostingCode1
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing dataCode1
Data Models for Dataset Drift Controls in Machine Learning With Optical ImagesCode1
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