java 在java中制作决策树的最佳学习算法?

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时间:2020-10-29 17:05:11  来源:igfitidea点击:

Best learning algorithm to make a decision tree in java?

javaalgorithmmachine-learningclassificationdecision-tree

提问by X-Blaster

I have a datasets with information like age, city, age of children, ... and a result (confirm, accept).

我有一个数据集,其中包含诸如年龄、城市、儿童年龄等信息以及结果(确认、接受)。

To help modelisation of "workflow", I want to create automatically a decision tree based on previous datasets.

为了帮助“工作流”建模,我想根据以前的数据集自动创建一个决策树。

I have take a look at http://en.wikipedia.org/wiki/Decision_tree_learningand I know that the problem is clearly not obvious.

我查看了http://en.wikipedia.org/wiki/Decision_tree_learning,我知道问题显然并不明显。

I just want to have advice on some algorithm or some libs on this subject what can help me in the contruction of a decision tree based on samples.

我只是想就这个主题的一些算法或一些库提出建议,什么可以帮助我基于样本构建决策树。

回答by Nate Kohl

You should take a look at Weka, a free Java-based supervised learning suite.

你应该看看Weka,一个免费的基于 Java 的监督学习套件。

After converting your data into Weka's simple text-based .arff format, you should be able to use the GUI or command-line interface to train and test a variety of different classifiers on that data, including:

将您的数据转换为 Weka 简单的基于文本的.arff 格式后,您应该能够使用 GUI 或命令行界面来训练和测试该数据的各种不同分类器,包括:

  • decision trees
  • neural networks
  • rule-based systems
  • support vector machines (SVMs)
  • various types of regression
  • 决策树
  • 神经网络
  • 基于规则的系统
  • 支持向量机 (SVM)
  • 各种类型的回归

Experimenting with this interface should allow you to easily try different classifiers and training parameters to determine which ones perform the best on your data.

尝试使用此界面应该可以让您轻松尝试不同的分类器和训练参数,以确定哪些分类器和训练参数对您的数据表现最佳。

You can also use an API to integrate Weka into your own source code.

您还可以使用 API 将 Weka 集成到您自己的源代码中

回答by Marcin

If you want to compare performance of different types of decision trees from Weka, see benchmark results collected in TunedIT.org:

如果您想比较 Weka 中不同类型决策树的性能,请参阅 TunedIT.org 中收集的基准测试结果:

http://tunedit.org/results?d=UCI&a=Weka*tree.

http://tunedit.org/results?d=UCI&a=Weka*tree

Play with drop-down lists and name patterns of algorithms/datasets to choose which results should be presented.

使用下拉列表和算法/数据集的名称模式来选择应显示哪些结果。