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 511–520 of 2050 papers

TitleStatusHype
EPP: interpretable score of model predictive powerCode0
An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced ClassificationCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
Bayesian Joint Spike-and-Slab Graphical LassoCode0
Differentiable Model Selection for Ensemble LearningCode0
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and CostCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Execution-based Evaluation for Data Science Code Generation ModelsCode0
Finding the Homology of Decision Boundaries with Active LearningCode0
Adaptive multi-penalty regularization based on a generalized Lasso pathCode0
Show:102550
← PrevPage 52 of 205Next →

No leaderboard results yet.