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 1731–1740 of 2050 papers

TitleStatusHype
Automated Model Selection for Tabular DataCode0
Model Selection for Cross-Lingual TransferCode0
Scikit-learn: Machine Learning in PythonCode0
Tune: A Research Platform for Distributed Model Selection and TrainingCode0
Phase Transitions and a Model Order Selection Criterion for Spectral Graph ClusteringCode0
Don't Waste Your Time: Early Stopping Cross-ValidationCode0
Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity ModelsCode0
GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnaceCode0
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-LearningCode0
What the HellaSwag? On the Validity of Common-Sense Reasoning BenchmarksCode0
Show:102550
← PrevPage 174 of 205Next →

No leaderboard results yet.