具有錯誤發現率和型一誤差控制的可解釋之預測樹模型

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.