博碩士論文 108221601 完整後設資料紀錄

DC 欄位	值	語言
DC.contributor	數學系	zh_TW
DC.creator	李思娜	zh_TW
DC.creator	Intan Lisnawati	en_US
dc.date.accessioned	2022-9-30T07:39:07Z
dc.date.available	2022-9-30T07:39:07Z
dc.date.issued	2022
dc.identifier.uri	http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=108221601
dc.contributor.department	數學系	zh_TW
DC.description	國立中央大學	zh_TW
DC.description	National Central University	en_US
dc.description.abstract	给定一些输入，我们想要对相应的输出进行预测。为了对单个估计器做出 更好的预测，集成方法结合了使用给定学习算法构建的几个基本估计器的预 测。还可以调整每种方法的参数，以使真实值和预测值之间的差距更小。通 过使用房屋销售价格训练数据集，我们应用一些集成方法来预测看不见的房 屋销售价格数据集，并根据它们的均方根误差(RMSE) 值查看准确性。结果 表明，Gradient Boosting 给出的RMSE 最小，为22,766 美元，同时随机森林 为23,269 美元，XGBoost 为24,069 美元，决策树为35,637 美元	zh_TW
dc.description.abstract	Given some input, we want to make a prediction for the correspond- ing output. In order to make a better prediction over a single estimator, ensemble methods combine the predictions of several base estimators built with a given learning algorithm. Each method’s parameter also can be adjusted in order to get a closer gap between the real and the predicted value. By using House Sale Price training data set, we apply some ensem- ble methods to predict the unseen House Sale Price data set and see the accuracy based on their Root Mean Squared Error (RMSE) value. It shows that Gradient Boosting gives the smallest RMSE, US$ 22,766, meanwhile Random Forest US$ 23,269, XGBoost US$ 24,069, and Decision Tree US$ 35,637.	en_US
DC.subject	预测	zh_TW
DC.subject	合奏法	zh_TW
DC.subject	基础学习者	zh_TW
DC.subject	损失函数	zh_TW
DC.subject	prediction	en_US
DC.subject	ensemble method	en_US
DC.subject	base learner	en_US
DC.subject	loss function	en_US
DC.title	基於樹的集成方法在房屋銷售價格預測中的應用	zh_TW
dc.language.iso	zh-TW	zh-TW
DC.title	Tree-Based Ensemble Methods with an Application in House Sale Price Prediction	en_US
DC.type	博碩士論文	zh_TW
DC.type	thesis	en_US
DC.publisher	National Central University	en_US