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

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.