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 311–320 of 2050 papers

TitleStatusHype
A general technique for the estimation of farm animal body part weights from CT scans and its applications in a rabbit breeding programCode0
A principled approach to model validation in domain generalizationCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
Approximate Cross-validation: Guarantees for Model Assessment and SelectionCode0
EPP: interpretable score of model predictive powerCode0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender SystemsCode0
Show:102550
← PrevPage 32 of 205Next →

No leaderboard results yet.