I have a datasets with information like age, city, age of children, ... and a result (confirm, accept).
我有数据集，包括年龄，城市，孩子的年龄……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”的建模，我想基于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_learning and I know that the problem is clearly not obvious.
我查看了http://en.wikipedia.org/wiki/Decision_tree_learning，我知道这个问题显然不明显。
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.
我只是想给大家一些关于这个问题的算法或者libs的建议什么可以帮助我构建一个基于样本的决策树。
2 个解决方案
#1
14
You should take a look at Weka, a free Java-based supervised learning suite.
您应该看看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:
在将数据转换为Weka简单的基于文本的.arff格式之后，您应该能够使用GUI或命令行界面对数据进行训练和测试，包括:
- decision trees
- 决策树
- neural networks
- 神经网络
- rule-based systems
- 基于规则的系统
- support vector machines (SVMs)
- 支持向量机(svm)
- various types of regression
- 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.
使用这个接口进行实验应该可以让您轻松地尝试不同的分类器和训练参数，以确定哪些分类器在您的数据上执行得最好。
You can also use an API to integrate Weka into your own source code.
您还可以使用API将Weka集成到自己的源代码中。
#2
2
If you want to compare performance of different types of decision trees from Weka, see benchmark results collected in TunedIT.org:
如果您想要比较Weka中不同类型决策树的性能，请参见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 *树。
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该显示的结果。
#1
14
You should take a look at Weka, a free Java-based supervised learning suite.
您应该看看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:
在将数据转换为Weka简单的基于文本的.arff格式之后，您应该能够使用GUI或命令行界面对数据进行训练和测试，包括:
- decision trees
- 决策树
- neural networks
- 神经网络
- rule-based systems
- 基于规则的系统
- support vector machines (SVMs)
- 支持向量机(svm)
- various types of regression
- 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.
使用这个接口进行实验应该可以让您轻松地尝试不同的分类器和训练参数，以确定哪些分类器在您的数据上执行得最好。
You can also use an API to integrate Weka into your own source code.
您还可以使用API将Weka集成到自己的源代码中。
#2
2
If you want to compare performance of different types of decision trees from Weka, see benchmark results collected in TunedIT.org:
如果您想要比较Weka中不同类型决策树的性能，请参见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 *树。
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该显示的结果。