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 1881–1890 of 2050 papers

TitleStatusHype
Beyond One-Size-Fits-All: Tailored Benchmarks for Efficient EvaluationCode0
Diagnostic Tool for Out-of-Sample Model EvaluationCode0
Automated Dependence PlotsCode0
Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph ClusteringCode0
A general technique for the estimation of farm animal body part weights from CT scans and its applications in a rabbit breeding programCode0
Quality Estimation for Image Captions Based on Large-scale Human EvaluationsCode0
Towards Model Selection using Learning Curve Cross-ValidationCode0
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect EstimationCode0
DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy ClassificationCode0
Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval Augmented Generation SystemsCode0
Show:102550
← PrevPage 189 of 205Next →

No leaderboard results yet.