基于特征工程的建设工程造价指数预测模型构建

对建设工程造价指数的预测能够有效解决建设项目前期投资估算误差较大引起的成本问题。结合实际工程中对造价指数预测模型的需求,以U市发布的2012—2021年建设工程造价指数为例,通过对比不同特征工程方法构建的XGBoost和神经网络两者之间预测误差,选择最优预测模型进行建设工程造价指数模型预测。结果表明,基于树模型特征筛选和均值填充数据集的XGBoost模型,在测试集、训练集、交叉验证误差最低,能够作为建设工程造价指数预测的模型。

Construction Cost Index Prediction Model Based on Feature Engineering

The prediction of construction project cost index can effectively solve the cost problems caused by large errors in the preliminary investment estimation of construction projects. Combining the demand for construction cost index prediction models in actual projects, the construction cost index for 2012-2021 released by U city was used as an example to select the optimal prediction model for construction cost index model prediction by comparing the prediction errors between XGBoost and neural network constructed by different feature engineering methods. The results show that the XGBoost model based on tree model feature screening and mean-populated data set has the lowest error in the test set, training set and cross-validation, and can be used as a model for construction cost index prediction.