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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

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Showing 581–590 of 2050 papers

TitleStatusHype
Estimating Stable Fixed Points and Langevin Potentials for Financial Dynamics—0
Improving VTE Identification through Adaptive NLP Model Selection and Clinical Expert Rule-based Classifier from Radiology Reports—0
The Topology and Geometry of Neural RepresentationsCode0
Error Reduction from Stacked Regressions—0
DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy ClassificationCode0
ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis TestingCode4
Towards Last-layer Retraining for Group Robustness with Fewer AnnotationsCode1
A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Models—0
Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message LengthCode0
A novel algebraic approach to time-reversible evolutionary modelsCode0
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