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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 551–560 of 2050 papers

TitleStatusHype
Comparative Study of Machine Learning Algorithms in Detecting Cardiovascular Diseases—0
The Economic Implications of Large Language Model Selection on Earnings and Return on Investment: A Decision Theoretic Model—0
IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmarkCode0
Cross-Validated Off-Policy EvaluationCode0
AnyLoss: Transforming Classification Metrics into Loss FunctionsCode0
Symmetric Linear Bandits with Hidden SymmetryCode0
Green AI in Action: Strategic Model Selection for Ensembles in Production—0
Simultaneous Identification of Sparse Structures and Communities in Heterogeneous Graphical Models—0
Comparative Analysis of Predicting Subsequent Steps in Hénon Map—0
Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations—0
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