Weka机器学习:如何解释朴素贝叶斯分类器? [英] Weka machine learning:how to interprete Naive Bayes classifier?

问题描述

我正在使用资源管理器功能进行分类.我的.arff数据文件具有10个数字和二进制值功能； (只有实例ID是标称的).我有16个实例.可以预测的课程是是/否.我使用过朴素贝叶斯，但我无法解释结果，有人知道如何解释朴素贝叶斯分类的结果吗?

I am using the explorer feature for classification. My .arff data file has 10 features of numeric and binary values; (only the ID of instances is nominal).I have abt 16 instances. The class to predict is Yes/No.i have used Naive bayes but i cantnot interpret the results,,does anyone know how to interpret results from naive Bayes classification?

推荐答案

朴素贝叶斯未选择任何重要功能.如您所述，训练朴素贝叶斯分类器的结果是每个特征的均值和方差.将新样本分为是"或否"是基于样本的特征值是否与是"或否"的训练特征的均值和方差最匹配.

Naive Bayes doesn't select any important features. As you mentioned, the result of the training of a Naive Bayes classifier is the mean and variance for every feature. The classification of new samples into 'Yes' or 'No' is based on whether the values of features of the sample match best to the mean and variance of the trained features for either 'Yes' or 'No'.

您可以使用其他算法来找到最有用的属性.在这种情况下，您可能要使用决策树分类器，例如WEKA中的J48(这是 C4.5决策树算法的开源实现) .最终决策树中的第一个节点会告诉您哪个功能具有最大的预测能力.

You could use others algorithms to find the most informative attributes. In that case you might want to use a decision tree classifier, e.g. J48 in WEKA (which is the open-source implementation of C4.5 decision tree algorithm). The first node in the resulting decision tree tells you which feature has the most predictive power.

更好(如另一篇文章中的Rushdi Shams所述)； Weka的资源管理器提供了专用的构建选项，可以在数据集中找到最有用的属性.这些选项可以在Select attributes标签下找到.

Even better (as stated by Rushdi Shams in the other post); Weka's Explorer offers purpose build options to find the most useful attributes in a dataset. These options can be found under the Select attributes tab.

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