Optimizing Ensemble Weights for Machine Learning Models: A Case Study for Housing Price Prediction

Mohsen Shahhosseini, Guiping Hu () and Hieu Pham
Additional contact information
Mohsen Shahhosseini: Iowa State University Ames
Guiping Hu: Iowa State University Ames
Hieu Pham: Iowa State University Ames

A chapter in Smart Service Systems, Operations Management, and Analytics, 2020, pp 87-97 from Springer

Abstract: Abstract Designing ensemble learners has been recognized as one of the significant trends in the field of data knowledge, especially, in data science competitions. Building models that are able to outperform all individual models in terms of bias, which is the error due to the difference in the average model predictions and actual values, and variance, which is the variability of model predictions, has been the main goal of the studies in this area. An optimization model has been proposed in this paper to design ensembles that try to minimize bias and variance of predictions. Focusing on service sciences, two well-known housing datasets have been selected as case studies: Boston housing and Ames housing. The results demonstrate that our designed ensembles can be very competitive in predicting the house prices in both Boston and Ames datasets.

Keywords: Machine learning; Optimal ensemble; Bias-Variance trade-off; House price prediction (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-030-30967-1_9

Ordering information: This item can be ordered from
http://www.springer.com/9783030309671

DOI: 10.1007/978-3-030-30967-1_9

Access Statistics for this chapter

More chapters in Springer Proceedings in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().