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  4. 隨機森林的理論及應用範例說明
 
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隨機森林的理論及應用範例說明

Other Title
The Theory and Illustration of Application of Random Forest
Journal
作物、環境與生物資訊
Journal Volume
17
Journal Issue
1
Pages
1-12
Date Issued
2020-03
Author(s)
林詠淳
康樂  
郭寶錚
Lin, Yung-chun
Le Kang  
Kuo, Bo-jein
DOI
10.30061/CEB.202001_17(1).001
URI
https://scholars.tari.gov.tw/handle/123456789/18252
Abstract
隨著大據時代的來臨,如何從大量的資料中萃取出有價值的訊息顯得格外重要,因此發展出一套能透過樣本訓練機器辨識和學習的機器學習演算法,來有效處理日益繁雜的大量資料。 With the advent of big data generation, it is and important task to extract valuable information from a large amount of data. Therefore, different types of machine learning algorithms that can train machine recognition and learning from samples have been developed.
Subjects
機器學習
隨機森林
資料分析
Machine learning
Random forest
Data analysis
Publisher
台灣農藝學會、中華農業氣象學會
Type
journal article

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