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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 1681–1690 of 2050 papers

TitleStatusHype
Causal Covariate Shift Correction using Fisher information penalty—0
Causal Discovery in Hawkes Processes by Minimum Description Length—0
Causal Falling Rule Lists—0
Causal Q-Aggregation for CATE Model Selection—0
Choice modelling in the age of machine learning - discussion paper—0
Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation—0
Choosing a Model, Shaping a Future: Comparing LLM Perspectives on Sustainability and its Relationship with AI—0
Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion—0
CLAMS: A System for Zero-Shot Model Selection for Clustering—0
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection—0
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