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

In: Smart Service Systems, Operations Management, and Analytics

Author

Listed:
  • Mohsen Shahhosseini

    (Iowa State University Ames)

  • Guiping Hu

    (Iowa State University Ames)

  • Hieu Pham

    (Iowa State University Ames)

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.

Suggested Citation

  • Mohsen Shahhosseini & Guiping Hu & Hieu Pham, 2020. "Optimizing Ensemble Weights for Machine Learning Models: A Case Study for Housing Price Prediction," Springer Proceedings in Business and Economics, in: Hui Yang & Robin Qiu & Weiwei Chen (ed.), Smart Service Systems, Operations Management, and Analytics, pages 87-97, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-30967-1_9
    DOI: 10.1007/978-3-030-30967-1_9
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