Question

我一直在使用Weka的J48决策树来对关键字的频率进行分类在RSS中提供目标类别。我想我可能有问题将生成的决策树与正确分类的数量进行协调报告的实例和混淆矩阵。

例如，我的一个.arff文件包含以下数据提取：

@attribute Keyword_1_nasa_Frequency numeric
@attribute Keyword_2_fish_Frequency numeric
@attribute Keyword_3_kill_Frequency numeric
@attribute Keyword_4_show_Frequency numeric
...
@attribute Keyword_64_fear_Frequency numeric
@attribute RSSFeedCategoryDescription {BFE,FCL,F,M, NCA, SNT,S}

@data
0,0,0,34,0,0,0,0,0,40,0,0,0,0,0,0,0,0,0,0,24,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,BFE
0,0,0,10,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,BFE
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,BFE
...
20,0,64,19,0,162,0,0,36,72,179,24,24,47,24,40,0,48,0,0,0,97,24,0,48,205,143,62,78,
0,0,216,0,36,24,24,0,0,24,0,0,0,0,140,24,0,0,0,0,72,176,0,0,144,48,0,38,0,284,
221,72,0,72,0,SNT
...
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,S

依此类推：总共有64个关键字（列）和570行，其中每一行都包含一天中Feed中关键字的频率。在这种情况下，有57个饲料 10天给出总共570条记录。每个关键字都带有前缀带有代理号，后缀为“频率”。

我对决策树的使用是使用10x验证的默认参数。

Weka报道以下内容：

Correctly Classified Instances         210               36.8421 %
Incorrectly Classified Instances       360               63.1579 %

使用以下混淆矩阵：

=== Confusion Matrix ===

   a   b   c   d   e   f   g   <-- classified as
  11   0   0   0  39   0   0 |   a = BFE
   0   0   0   0  60   0   0 |   b = FCL
   1   0   5   0  72   0   2 |   c = F
   0   0   1   0  69   0   0 |   d = M
   3   0   0   0 153   0   4 |   e = NCA
   0   0   0   0  90  10   0 |   f = SNT
   0   0   0   0  19   0  31 |   g = S

树如下：

Keyword_22_health_Frequency <= 0
|   Keyword_7_open_Frequency <= 0
|   |   Keyword_52_libya_Frequency <= 0
|   |   |   Keyword_21_job_Frequency <= 0
|   |   |   |   Keyword_48_pic_Frequency <= 0
|   |   |   |   |   Keyword_63_world_Frequency <= 0
|   |   |   |   |   |   Keyword_26_day_Frequency <= 0: NCA (461.0/343.0)
|   |   |   |   |   |   Keyword_26_day_Frequency > 0: BFE (8.0/3.0)
|   |   |   |   |   Keyword_63_world_Frequency > 0
|   |   |   |   |   |   Keyword_31_gaddafi_Frequency <= 0: S (4.0/1.0)
|   |   |   |   |   |   Keyword_31_gaddafi_Frequency > 0: NCA (3.0)
|   |   |   |   Keyword_48_pic_Frequency > 0: F (7.0)
|   |   |   Keyword_21_job_Frequency > 0: BFE (10.0/1.0)
|   |   Keyword_52_libya_Frequency > 0: NCA (31.0)
|   Keyword_7_open_Frequency > 0
|   |   Keyword_31_gaddafi_Frequency <= 0: S (32.0/1.0)
|   |   Keyword_31_gaddafi_Frequency > 0: NCA (4.0)
Keyword_22_health_Frequency > 0: SNT (10.0)

我的问题涉及将矩阵与树协调，反之亦然。据，直到...为止我理解结果，像（461.0 / 343.0）这样的评级表明461个实例被归类为NCA。但是，当矩阵仅显示153时，怎么可能呢？我是不知道怎么解释这个，所以欢迎任何帮助。

提前致谢。

Answer 1

每个叶子的括号中的数字应该被理解为（此叶子上此分类的总实例数/此叶子中不正确分类的数量）。

在你的第一个NCA叶子的例子中，它说有461个测试实例被归类为NCA，而在那些461中，有343个不正确的分类。因此，该叶子上有461-343 = 118个正确分类的实例。

查看您的决策树，请注意NCA也处于其他位置。我统计了118 + 3 + 31 + 4 = 156个正确分类的实例，其中461 + 3 + 31 + 4 = 499个NCA总分类。

您的混淆矩阵在39 + 60 + 72 + 69 + 153 + 90 + 19 = 502 NCA总分类中显示了153个正确的NCA分类。

因此树（156/499）与您的混淆矩阵（153/502）之间存在细微差别。

请注意，如果您从命令行运行Weka，它会输出一个树和一个混淆矩阵，用于测试所有训练数据以及使用交叉验证进行测试。请注意，您正在查看正确的树的正确矩阵。 Weka输出训练和测试结果，产生两对矩阵和树。你可能把它们搞混了。

决策树中正确/错误分类的实例与Weka中的混淆矩阵之间的差异

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