隨機森林與梯度提升決策樹在大數據下之探討

A Study of Random Forests and Gradient Boosting Decision Trees for Large-Scale Data

陳聖惟(Sheng-Wei Chen)

指導教授 ： 林智仁

國立臺灣大學/工學院/工業工程學研究所/碩士(2017年)

https://doi.org/10.6342/NTU201702065

摘要

無資料

關鍵字

梯度提升決策樹 ； 隨機森林 ； 支持向量機 ； 分類與回歸樹

並列摘要

In the past, we know that the tree-based methods may not handle the large-scale data sets. Therefore, the solver of the gradient boosting decision trees performs excellent in the large-scale data competitions. To know the details, we analyze the models of these tree-based methods. Furthermore, we compare their test accuracy and training time, we also consider the linear model and kernel method.

並列關鍵字

gradient boosting decision trees ； random forests ； support vector machine ； classification and regression tree

參考文獻

L. Breiman. Bagging predictors. Machine Learning, 24(2):123–140, August 1996.

L. Breiman, J. Friedman, C. J. Stone, and R. Olshen. Classification and Regression Trees. Chapman and Hall/CRC; 1 edition, 1984.

C. Cortes and V. Vapnik. Support-vector network. Machine Learning, 20:273–297, 1995.

R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: a library for large linear classification. Journal of Machine Learning Research, 9:1871–1874, 2008. URL http://www.csie.ntu.edu.tw/cjlin/papers/liblinear.pdf.

Additive logistic regression: a Statistical view of boosting (with discussion and a rejoinder by the authors).