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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 901–910 of 2050 papers

TitleStatusHype
Aggregation of Affine Estimators—0
A coupled-mechanisms modelling framework for neurodegeneration—0
Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT—0
How to select predictive models for causal inference?—0
Improving classification performance by feature space transformations and model selection—0
Causal Discovery in Hawkes Processes by Minimum Description Length—0
Feature Selection Methods for Cost-Constrained Classification in Random Forests—0
hv-Block Cross Validation is not a BIBD: a Note on the Paper by Jeff Racine (2000)—0
Hybrid methodology based on Bayesian optimization and GA-PARSIMONY to search for parsimony models by combining hyperparameter optimization and feature selection—0
Feature-based model selection for object detection from point cloud data—0
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