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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 1481–1490 of 2050 papers

TitleStatusHype
On the overestimation of widely applicable Bayesian information criterion—0
Model Selection With Graphical Neighbour Information—0
EPP: interpretable score of model predictive powerCode0
Parkinson's Disease Recognition Using SPECT Image and Interpretable AI: A Tutorial—0
Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms—0
Minimum Description Length Revisited—0
Hybrid methodology based on Bayesian optimization and GA-PARSIMONY to search for parsimony models by combining hyperparameter optimization and feature selection—0
3D Rigid Motion Segmentation with Mixed and Unknown Number of Models—0
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools—0
metric-learn: Metric Learning Algorithms in PythonCode0
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