基于分类回归决策树算法的航班延误预测模型

Flight delay prediction model based on CART algorithm

针对民航客机航班延误问题,构建了基于随机森林(random forest)与分类回归决策树(CART,classification and regression tree)算法的航班延误预测模型,利用国内大型机场的真实数据集对模型进行训练,通过与Logistic回归算法,K-近邻回归(KNN,K-nearest neighbor)算法和决策树(decision tree)算法的训练结果对比,从拟合效果可以看出,该方法可以处理高维度数据,泛化能力好,降低了过拟合的可能性,模型的拟合程度R2可以达到0.83。

Aiming at the flight delay problem of civil aviation airliners,this paper constructs a flight delay prediction model based on random forest model and classification regression decision tree(CART)algorithm,on which a large amount of training are conducted by using the real data set from large China airports.With the training results of Logistic regression algorithm,K-nearest neighbor(KNN)regression algorithm and decision tree algorithm,and with the fitting results,it is concluded that this method can deal with high latitude data with good generalization ability,and reduce the possibility of over fitting.The fitting degree R^(2) of the model can reach 0.83.