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曾建翔
分析多維計數資料的樹狀方法
分析多維計數資料的樹狀方法
分析多維計數資料的樹狀方法
By
曾建翔
・
Tainan City, Taiwan
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現在AI時代,有關機器學習演算法已在網路上熱烈討論及探討,而討論度最高的兩類演算法莫過於神經網路演算法及樹狀演算法,其中樹狀演算法相對容易理解、構建及解釋,被廣泛應用於統計學、資料探勘與機器學習。 大部分的樹狀方法都只能分析單維度的資料,有關研究多維度資料上的樹狀方法的文獻並不多,而在不同領域裡,人們往往對不同事件發生的次數感興趣,例如醫學領域裡觀察病人的門診次數及住院次數受何種因素影響最大,以對症下藥。 我們的方法在分析一份資料為 1987 年和 1988 年進行的美國國家醫療支出調查,同時使用兩種反應變數"醫師就診次數"與"急診室就診次數 "發現相較單維度數狀方法多了一個分類解釋變數為"受教育年限"小於11年對於自我健康意識不強,因此若針對這部分加強健康教育,便有利於改善醫療支出。
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曾建翔
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Published: Aug 18th 2020
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