SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 521–530 of 2050 papers

TitleStatusHype
Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection—0
Improved identification accuracy in equation learning via comprehensive R^2-elimination and Bayesian model selectionCode0
Machine-Guided Discovery of a Real-World Rogue Wave ModelCode1
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
How False Data Affects Machine Learning Models in Electrochemistry?Code0
Supervised structure learning—0
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
Show:102550
← PrevPage 53 of 205Next →

No leaderboard results yet.