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 841–850 of 2050 papers

TitleStatusHype
Bayesian CART models for insurance claims frequency—0
In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised LearningCode0
Hyperparameter Tuning and Model Evaluation in Causal Effect EstimationCode0
A Vision for Semantically Enriched Data Science—0
FedScore: A privacy-preserving framework for federated scoring system developmentCode0
A novel efficient Multi-view traffic-related object detection framework—0
Detecting Signs of Model Change with Continuous Model Selection Based on Descriptive Dimensionality—0
Pseudo-Labeling for Kernel Ridge Regression under Covariate ShiftCode0
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles—0
Evaluating Representations with Readout Model Switching—0
Show:102550
← PrevPage 85 of 205Next →

No leaderboard results yet.