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 1271–1280 of 2050 papers

TitleStatusHype
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets—0
How to select predictive models for causal inference?—0
How to Select Pre-Trained Code Models for Reuse? A Learning Perspective—0
hv-Block Cross Validation is not a BIBD: a Note on the Paper by Jeff Racine (2000)—0
Hybrid methodology based on Bayesian optimization and GA-PARSIMONY to search for parsimony models by combining hyperparameter optimization and feature selection—0
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets—0
Hypothesis Testing for Equality of Latent Positions in Random Graphs—0
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory—0
ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets—0
Identification of Physical Processes and Unknown Parameters of 3D Groundwater Contaminant Problems via Theory-guided U-net—0
Show:102550
← PrevPage 128 of 205Next →

No leaderboard results yet.