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 1261–1270 of 2050 papers

TitleStatusHype
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting—0
High-Dimensional Dynamic Covariance Models with Random Forests—0
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions—0
High-Dimensional Importance-Weighted Information Criteria: Theory and Optimality—0
Higher-order asymptotics for the parametric complexity—0
High SNR Consistent Compressive Sensing—0
Homotopy Continuation Approaches for Robust SV Classification and Regression—0
Housing Price Prediction Model Selection Based on Lorenz and Concentration Curves: Empirical Evidence from Tehran Housing Market—0
How do some Bayesian Network machine learned graphs compare to causal knowledge?—0
How Many Communities Are There?—0
Show:102550
← PrevPage 127 of 205Next →

No leaderboard results yet.