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 251–260 of 2050 papers

TitleStatusHype
All models are wrong, some are useful: Model Selection with Limited LabelsCode0
Adaptation of uncertainty-penalized Bayesian information criterion for parametric partial differential equation discoveryCode0
Adaptive Concentration of Regression Trees, with Application to Random ForestsCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
Execution-based Evaluation for Data Science Code Generation ModelsCode0
Fairness and bias correction in machine learning for depression prediction: results from four study populationsCode0
GeMID: Generalizable Models for IoT Device IdentificationCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
A survey of probabilistic generative frameworks for molecular simulationsCode0
Show:102550
← PrevPage 26 of 205Next →

No leaderboard results yet.