Question

我正在尝试确定分类中的变量重要性，并在Weka中使用ClassifierAttributeEVal选项。我选择SMO作为分类器，得到以下结果：

=== Run information ===

Evaluator:    weka.attributeSelection.ClassifierAttributeEval -execution 
slots 1 -B weka.classifiers.functions.SMO -F 5 -T 0.01 -R 1 -E DEFAULT -- -C1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K 
"weka.classifiers.functions.supportVector.PolyKernel -E 1.0 -C 250007" calibrator "weka.classifiers.functions.Logistic -R 1.0E-8 -M -1 -num-decimal-places 4"
Search:       weka.attributeSelection.Ranker -T -1.7976931348623157E308 -N -1
Relation:     threatweka
Instances:    2210
Attributes:   5
          author
          threat
          target.section
          activity
          country
Evaluation mode:    10-fold cross-validation



=== Attribute selection 10 fold cross-validation (stratified), seed: 1 ===

average merit      average rank  attribute
0.202 +- 0.007    1   +- 0      1 author
0.158 +- 0.004    2   +- 0      3 target.section
0.145 +- 0.003    3   +- 0      4 activity
0.077 +- 0.005    4   +- 0      5 country

有人可以帮助我解释这个结果吗，平均水平和优点是什么？

属性选择WEKA中ClassifierAttributeEval的解释

0 个答案: