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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 681–690 of 2050 papers

TitleStatusHype
Selective machine learning of doubly robust functionals—0
Fast Approximate Bayesian Computation for Estimating Parameters in Differential Equations—0
ER2Score: LLM-based Explainable and Customizable Metric for Assessing Radiology Reports with Reward-Control Loss—0
Error Reduction from Stacked Regressions—0
Event Data Association via Robust Model Fitting for Event-based Object Tracking—0
Differentially Private Learning with Margin Guarantees—0
biastest: Testing parameter equality across different models in Stata—0
Estimating Optimal Policy Value in General Linear Contextual Bandits—0
Estimating Real Log Canonical Thresholds—0
Differentially Private Generalized Linear Models Revisited—0
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