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 1641–1650 of 2050 papers

TitleStatusHype
Better Model Selection with a new Definition of Feature Importance—0
Beyond Conjugacy for Chain Event Graph Model Selection—0
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes—0
Beyond One-Size-Fits-All: Multi-Domain, Multi-Task Framework for Embedding Model Selection—0
Beyond similarity assessment: Selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm—0
Local Projections Inference with High-Dimensional Covariates without Sparsity—0
Selective machine learning of doubly robust functionals—0
biastest: Testing parameter equality across different models in Stata—0
Binary Matrix Factorization via Dictionary Learning—0
Bitcoin Forecasting Using ARIMA and PROPHET—0
Show:102550
← PrevPage 165 of 205Next →

No leaderboard results yet.