Overview of use of decision tree algorithms in machine learning

Arundhati Navada, A. N. Ansari, Siddharth Patil, B. Sonkamble
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引用次数: 144

Abstract

A decision tree is a tree whose internal nodes can be taken as tests (on input data patterns) and whose leaf nodes can be taken as categories (of these patterns). These tests are filtered down through the tree to get the right output to the input pattern. Decision Tree algorithms can be applied and used in various different fields. It can be used as a replacement for statistical procedures to find data, to extract text, to find missing data in a class, to improve search engines and it also finds various applications in medical fields. Many Decision tree algorithms have been formulated. They have different accuracy and cost effectiveness. It is also very important for us to know which algorithm is best to use. The ID3 is one of the oldest Decision tree algorithms. It is very useful while making simple decision trees but as the complications increases its accuracy to make good Decision trees decreases. Hence IDA (intelligent decision tree algorithm) and C4.5 algorithms have been formulated.
概述决策树算法在机器学习中的应用
决策树是一棵树,其内部节点可以作为测试(对输入数据模式),其叶节点可以作为分类(对这些模式)。这些测试通过树向下过滤,以获得输入模式的正确输出。决策树算法可以应用于各种不同的领域。它可以作为统计程序的替代品来查找数据,提取文本,查找类中的缺失数据,改进搜索引擎,并且在医学领域也有各种应用。许多决策树算法已经形成。它们具有不同的准确性和成本效益。对于我们来说,知道哪种算法是最好的也是非常重要的。ID3是最古老的决策树算法之一。它在制作简单决策树时非常有用,但随着复杂性的增加,制作好的决策树的准确性会降低。因此制定了智能决策树算法(IDA)和C4.5算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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