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 1451–1460 of 2050 papers

TitleStatusHype
A Base Model Selection Methodology for Efficient Fine-Tuning—0
On summarized validation curves and generalization—0
Interpretable Models for Understanding Immersive Simulations—0
The column measure and Gradient-Free Gradient Boosting—0
Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis—0
How have German University Tuition Fees Affected Enrollment Rates: Robust Model Selection and Design-based Inference in High-Dimensions—0
Weighted Sampling for Combined Model Selection and Hyperparameter Tuning—0
Not again! Data Leakage in Digital Pathology—0
Predictive Multiplicity in ClassificationCode0
Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization—0
Show:102550
← PrevPage 146 of 205Next →

No leaderboard results yet.