Seke Blog: 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

Seke Blog

weka.classifiers.rules.ZeroR

沒有留言:

Linked Lists from C to Java

總網頁瀏覽量

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