建造毕达哥拉斯作为随机想象森林为决策树建构

Erlin Windia Ambarsari, Herlinda Herlinda
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引用次数: 0

摘要

接收2020年6月24日修订2020年10月14日接受2020年10月26日学生观察毕达哥拉斯使用平面几何和三维几何。然而,也可以为决策树构建毕达哥拉斯。我们的关于Instagram使用习惯的研究与构造一个单一决策树的毕达哥拉斯。研究得到的结果是模糊的属性值。因此,继续研究随机森林的毕达哥拉斯模型。研究的目的是为了方便对属性中包含的模糊数据进行跟踪。结果得出目标类特征之间的关系,从而导致误分类。此错误导致无效数据;例如,对于一组20人的年龄目标,同一属性上的数据间隔为三倍。然而,尽管存在无效数据导致的错误分类,但基于随机森林的毕达哥拉斯构造,数据更容易追踪到错误,这是单一决策树无法完成的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Membangun Pythagoras Sebagai Visualisasi Random Forest Untuk Pemodelan Pohon Keputusan
Received June 24, 2020 Revised Oct 14, 2020 Accepted Oct 26, 2020 Students observed Pythagoras for using a plane Geometry and 3D Geometry. However, Pythagoras can also be built for decision trees. Our research regarding Instagram Usage Habit with construct Pythagoras for a single decision tree. The study's results obtained are ambiguous attribute values. Therefore, it is continued with research to build Pythagoras for Random Forest. The purpose of the study is to facilitate the tracking of ambiguous data contained in the attributes. The results obtained that the relationship between characteristics of the target class, thus resulting in misclassification. This error caused invalid data; for example, there are three times the separation of data on the same attribute for age's target for a group of 20. However, although there are misclassifications caused by invalid data, based on the Pythagorean construction for Random Forest, the data is more easily traced to errors, which cannot be done by a single decision tree.
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