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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 311–320 of 2050 papers

TitleStatusHype
Agreement-based Learning—0
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning—0
A Review of Cross-Sectional Matrix Exponential Spatial Models—0
Aggregation of Affine Estimators—0
A coupled-mechanisms modelling framework for neurodegeneration—0
A Review of Change of Variable Formulas for Generative Modeling—0
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods—0
AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow—0
A Regret-Variance Trade-Off in Online Learning—0
Area under the ROC Curve has the Most Consistent Evaluation for Binary Classification—0
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