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A novel construction cost prediction model using hybrid natural and light gradient boosting
Advanced Engineering Informatics ( IF 8.8 ) Pub Date : 2020-11-06 , DOI: 10.1016/j.aei.2020.101201
Debaditya Chakraborty , Hosam Elhegazy , Hazem Elzarka , Lilianna Gutierrez

In this paper, we compared the predictive capabilities of six different machine learning algorithms – linear regression, artificial neural network, random forest, extreme gradient boosting, light gradient boosting, and natural gradient boosting – and demonstrated that a hybrid light gradient boosting and natural gradient boosting model provides the most desirable construction cost estimates in terms of the accuracy metrics, uncertainty estimates, and training speed. We also present a game theory-based model interpretation technique to evaluate the average marginal contribution of each feature value, across all possible combinations of features, on the model predictions. The comparison between the predicted cost and the actual cost confirms good alignment with R2 0.99, RMSE 0.5, and MBE -0.009. Besides, the proposed hybrid model can provide uncertainty estimates through probabilistic predictions for real-valued outputs. This probabilistic prediction approach produces a holistic probability distribution over the entire outcome space to quantify the uncertainties related to construction cost predictions.



中文翻译:

混合自然光梯度提升的新型工程造价预测模型

在本文中,我们比较了六种不同的机器学习算法(线性回归,人工神经网络,随机森林,极限梯度增强,光梯度增强和自然梯度增强)的预测能力,并证明了混合光梯度增强和自然梯度提升模型在准确性指标,不确定性估计和训练速度方面提供了最理想的建设成本估计。我们还提出了一种基于博弈论的模型解释技术,以评估模型预测中所有特征值在所有可能组合之间的平均边际贡献。预测成本与实际成本之间的比较证实了与[R2 0.99, [R中号小号Ë 0.5,和 中号Ë -0.009。此外,所提出的混合模型可以通过对真实值输出的概率预测来提供不确定性估计。这种概率预测方法可在整个结果空间中产生整体概率分布,以量化与建筑成本预测相关的不确定性。

更新日期:2020-11-09
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