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 341–350 of 2050 papers

TitleStatusHype
When Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph BenchmarkCode1
Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPsCode1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval Augmented Generation SystemsCode0
On Leakage of Code Generation Evaluation Datasets—0
Comparative Evaluation of Learning Models for Bionic Robots: Non-Linear Transfer Function IdentificationsCode0
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
Comparative Analysis of LSTM Neural Networks and Traditional Machine Learning Models for Predicting Diabetes Patient Readmission—0
TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning BenchmarksCode4
Zero-shot prompt-based classification: topic labeling in times of foundation models in German Tweets—0
Show:102550
← PrevPage 35 of 205Next →

No leaderboard results yet.