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Title: | 具有錯誤發現率和型一誤差控制的可解釋之預測樹模型 A tree-based interpretable predictive method with FDR and type-one error control |
Authors: | Cheng-En Hong 洪晟恩 |
Advisor: | 歐陽彥正 |
Keyword: | 模型選擇,錯誤發現率, Knockoff,FDR,Lasso,Neyman-Pearson method, |
Publication Year : | 2017 |
Degree: | 碩士 |
Abstract: | 在實際的問題中儘管擁有許多的變數,我們並不曉得哪些變數是
真實的變數,哪些是虛假的雜訊。通過發現重要變數,研究人員可以 進一步利用選擇的重要變數進行更有針對性的後續實驗以利探討背 後的科學現象。一個自然的要求是,我們希望盡可能發現更多的相 關變量,同時盡可能犯更少的錯誤。我們提出一個改良的RuleFit 模 型,其中包含利用knockoff procedure 達到控制錯誤發現率, 以及通過 Neyman-Pearson 方法控制型一誤差。 Despite the abundance of the available variables, ground truth is privy to knowledge about the problem seldom revealed in practice. By discovering important features, researchers can further conduct a more targeted follow-up experiment on the selected features tailored for understanding the scientific phenomenon. A natural requirement is that we wish to discover as many relevant variables as possible and make as few mistakes as possible at the same time. We propose a modified RuleFit with FDR control by knockoff procedure and with alpha control by Neyman-Pearson method. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/20526 |
DOI: | 10.6342/NTU201702789 |
Fulltext Rights: | 未授權 |
Appears in Collections: | 統計碩士學位學程 |
Files in This Item:
File | Size | Format | |
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ntu-106-1.pdf Restricted Access | 1.18 MB | Adobe PDF |
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