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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 161–170 of 2050 papers

TitleStatusHype
Data-Informed Model Complexity Metric for Optimizing Symbolic Regression Models—0
DFPE: A Diverse Fingerprint Ensemble for Enhancing LLM PerformanceCode0
Dynamics of Transient Structure in In-Context Linear Regression Transformers—0
Quantifying Uncertainty and Variability in Machine Learning: Confidence Intervals for Quantiles in Performance Metric Distributions—0
Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST—0
Time Series Embedding Methods for Classification Tasks: A ReviewCode1
A Bayesian Modelling Framework with Model Comparison for Epidemics with Super-SpreadingCode0
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands—0
Statistical Inference for Sequential Feature Selection after Domain AdaptationCode0
Principled model selection for stochastic dynamics—0
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