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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 671–680 of 2050 papers

TitleStatusHype
Automatic Model Selection with Large Language Models for ReasoningCode1
Unraveling Cold Start Enigmas in Predictive Analytics for OTT Media: Synergistic Meta-Insights and Multimodal Ensemble Mastery—0
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model—0
Ranking & Reweighting Improves Group Distributional Robustness—0
Boldness-Recalibration for Binary Event Predictions—0
fairml: A Statistician's Take on Fair Machine Learning Modelling—0
Strengthening structural baselines for graph classification using Local Topological ProfileCode0
Revealing Similar Semantics Inside CNNs: An Interpretable Concept-based Comparison of Feature Spaces—0
Limits of Model Selection under Transfer Learning—0
ALMERIA: Boosting pairwise molecular contrasts with scalable methods—0
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