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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 1881–1890 of 2050 papers

TitleStatusHype
Episodic memory for continual model learning—0
ER2Score: LLM-based Explainable and Customizable Metric for Assessing Radiology Reports with Reward-Control Loss—0
Error Reduction from Stacked Regressions—0
Estimating Optimal Policy Value in General Linear Contextual Bandits—0
Estimating Real Log Canonical Thresholds—0
Estimating Stable Fixed Points and Langevin Potentials for Financial Dynamics—0
Estimating the Number of Components in Finite Mixture Models via Variational Approximation—0
Estimating the Number of Components in Panel Data Finite Mixture Regression Models with an Application to Production Function Heterogeneity—0
Estimation of Heterogeneous Treatment Effects Using a Conditional Moment Based Approach—0
Estimation of Local Average Treatment Effect by Data Combination—0
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