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Erschienen in:
2018 | OriginalPaper | Buchkapitel
15. Improving Model Performance
verfasst von : Ivo D. Dinov
Erschienen in: Data Science and Predictive Analytics
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Abstract
We already explored several alternative machine learning (ML) methods for prediction, classification, clustering, and outcome forecasting. In many situations, we derive models by estimating model coefficients or parameters. The main question now is How can we adopt the advantages of crowdsourcing and biosocial networking to aggregate different predictive analytics strategies? Are there reasons to believe that such ensembles of forecasting methods may actually improve the performance (e.g., increase prediction accuracy) of the resulting consensus meta-algorithm? In this chapter, we are going to introduce ways that we can search for optimal parameters for a single ML method as well as aggregate different methods into ensembles to enhance their collective performance relative to any of the individual methods part of the meta-aggregate.