English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Pro

Presents the zaia usage as premium feature

ZAIA

Presents AI Discovery search usage as a premium feature

AI Discovery

Presents AI Discovery explain and summary usage as a premium feature

Content Analysis

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Presents the pdf analysis as premium feature

PDF Analysis

1500 Top-up

3000 Top-up

Zendy Basic

2500 Top-up

Zendy Unlimited

Presents the access of premium content as premium feature

Premium Content

The goal of this paper is to compare several widely used Bayesian modelselection methods in practical model selection problems, highlight theirdifferences and give recommendations about the preferred approaches. We focuson the variable subset selection for regression and classification and performseveral numerical experiments using both simulated and real world data. Theresults show that the optimization of a utility estimate such as thecross-validation (CV) score is liable to finding overfitted models due torelatively high variance in the utility estimates when the data is scarce. Thiscan also lead to substantial selection induced bias and optimism in theperformance evaluation for the selected model. From a predictive viewpoint,best results are obtained by accounting for model uncertainty by forming thefull encompassing model, such as the Bayesian model averaging solution over thecandidate models. If the encompassing model is too complex, it can be robustlysimplified by the projection method, in which the information of the full modelis projected onto the submodels. This approach is substantially less prone tooverfitting than selection based on CV-score. Overall, the projection methodappears to outperform also the maximum a posteriori model and the selection ofthe most probable variables. The study also demonstrates that the modelselection can greatly benefit from using cross-validation outside the searchingprocess both for guiding the model size selection and assessing the predictiveperformance of the finally selected model.

Comparison of Bayesian predictive methods for model selection