2015年10月10日 星期六

weka.classifiers.rules.ZeroR

weka.classifiers.rules.ZeroR屬背景值(零規則)學習器,利用多數/平均決原理,提供案例集背景表現值供標竿比較之用。
任何學習器都應該比ZeroR表現(背景值)更好才有存在價值。

ZeroR學習分類時只記錄看過案例中多數類別為何。學習迴歸時只記錄看過案例的平均值為何。
預測時則完全不看案例屬性,任何案例的分類皆預測為記錄的多數類別,任何迴歸皆預測為記錄的平均值。

> java -cp weka.jar;. weka.classifiers.rules.ZeroR  -t data\weather.numeric.arff

ZeroR predicts class value: yes

Time taken to build model: 0 seconds
Time taken to test model on training data: 0 seconds

=== Error on training data ===

Correctly Classified Instances           9               64.2857 %
Incorrectly Classified Instances         5               35.7143 %
Kappa statistic                          0
Mean absolute error                      0.4643
Root mean squared error                  0.4795
Relative absolute error                100      %
Root relative squared error            100      %
Total Number of Instances               14


=== Confusion Matrix ===

 a b   <-- classified as
 9 0 | a = yes
 5 0 | b = no



=== Stratified cross-validation ===

Correctly Classified Instances           9               64.2857 %
Incorrectly Classified Instances         5               35.7143 %
Kappa statistic                          0
Mean absolute error                      0.4762
Root mean squared error                  0.4934
Relative absolute error                100      %
Root relative squared error            100      %
Total Number of Instances               14


=== Confusion Matrix ===

 a b   <-- classified as
 9 0 | a = yes
 5 0 | b = no

如下 weather.numeric.arff 案例集的14個案例有9個yes,5個no。
outlook temperature humidity windy play
sunny 85 85 FALSE no
sunny 80 90 TRUE no
rainy 65 70 TRUE no
sunny 72 95 FALSE no
rainy 71 91 TRUE no
overcast 83 86 FALSE yes
rainy 70 96 FALSE yes
rainy 68 80 FALSE yes
overcast 64 65 TRUE yes
sunny 69 70 FALSE yes
rainy 75 80 FALSE yes
sunny 75 70 TRUE yes
overcast 72 90 TRUE yes
overcast 81 75 FALSE yes
參考: weka.classifiers.rules.ZeorR 1. source code 2. documentation

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