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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 681–690 of 2050 papers

TitleStatusHype
GPT in Data Science: A Practical Exploration of Model Selection—0
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework—0
Supervised structure learning—0
How False Data Affects Machine Learning Models in Electrochemistry?Code0
To Translate or Not to Translate: A Systematic Investigation of Translation-Based Cross-Lingual Transfer to Low-Resource Languages—0
Comparison of model selection techniques for seafloor scattering statistics—0
Finite Mixtures of Multivariate Poisson-Log Normal Factor Analyzers for Clustering Count DataCode0
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?Code0
Unsupervised Video Summarization via Iterative Training and Simplified GANCode0
Saturn: Efficient Multi-Large-Model Deep Learning—0
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