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國立臺灣大學計量理論與應用研究中心 - CRETA

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CRETA Seminar

2024 年 9 月 26 日 CRETA Seminar - Analyze Additive and Interaction Effects via Collaborative Trees

訊息標題:

2024 年 9 月 26 日 CRETA Seminar - Analyze Additive and Interaction Effects via Collaborative Trees

簡介摘要:

國立臺灣大學計量理論與應用研究中心 (CRETA) ,國立臺灣大學經濟學系及臺灣經濟計量學會 (TES) 將於 2024 年 9 月 26 日舉辦 CRETA Seminar。相關資訊如下:

 

 9  26  CRETA Seminar

日期:2024 年 9 月 26 日 (週四) 下午 1:30~3:00

地點:國立臺灣大學管理學院二號館 3 樓 305 教室

講者:

紀建名教授 (中央研究院統計科學研究所)

演講主題:Analyze Additive and Interaction Effects via Collaborative Trees

 

講題摘要:We present Collaborative Trees, a novel tree model designed for regression prediction, along with its bagging version, which aims to analyze complex statistical associations between features and uncover potential patterns inherent in the data. We decompose the mean decrease in impurity from the proposed tree model to analyze the additive and interaction effects of features on the response variable. Additionally, we introduce network diagrams to visually depict how each feature contributes additively to the response and how pairs of features contribute interaction effects. Through a detailed demonstration using an embryo growth dataset, we illustrate how the new statistical tools aid data analysis, both visually and numerically. Moreover, we delve into critical aspects of tree modeling, such as prediction performance, inference stability, and bias in feature importance measures, leveraging real datasets and simulation experiments for comprehensive discussions. On the theory side, we show that Collaborative Trees, built upon a ''sum of trees'' approach with our own innovative tree model regularization, exhibit characteristics akin to matching pursuit, under the assumption of  high-dimensional independent binary input features (or one-hot feature groups). This newfound link sheds light on the superior capability of our tree model in estimating additive effects of features, a crucial factor for accurate interaction effect estimation.

ArXiv link:

 

[2405.11477] Analyze Additive and Interaction Effects via Collaborative Trees (arxiv.org)

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