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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 181–190 of 2050 papers

TitleStatusHype
BERTScore: Evaluating Text Generation with BERTCode1
LENSLLM: Unveiling Fine-Tuning Dynamics for LLM SelectionCode1
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible EvaluationCode1
LOVM: Language-Only Vision Model SelectionCode1
A General Model for Aggregating Annotations Across Simple, Complex, and Multi-Object Annotation TasksCode1
Machine Learning for Dynamic Resource Allocation in Network Function VirtualizationCode1
Evaluating Language Models as Synthetic Data GeneratorsCode1
mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in RCode1
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare SettingsCode1
NLP-ADBench: NLP Anomaly Detection BenchmarkCode1
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