weka.classifiers.functions.LinearRegression

weka.classifiers.functions.LinearRegression 為標準線性迴歸學習器，
學習各數值屬性的權重，建立線性方程式模型，預測數值類別。

參數說明:
-S select_attribute_code 屬性挑選法代碼，0 表M5'，1 表無，2 表Greedy。預設值 0。


> java  weka.classifiers.functions.LinearRegression  -t data\cpu.arff


Linear Regression Model

class =

      0.0491 * MYCT +
      0.0152 * MMIN +
      0.0056 * MMAX +
      0.6298 * CACH +
      1.4599 * CHMAX +
    -56.075 


Time taken to build model: 0.02 seconds
Time taken to test model on training data: 0.02 seconds

=== Error on training data ===

Correlation coefficient                  0.93
Mean absolute error                     37.9748
Root mean squared error                 58.9899
Relative absolute error                 39.592  %
Root relative squared error             36.7663 %
Total Number of Instances              209     



=== Cross-validation ===

Correlation coefficient                  0.9012
Mean absolute error                     41.0886
Root mean squared error                 69.556 
Relative absolute error                 42.6943 %
Root relative squared error             43.2421 %
Total Number of Instances              209     


cpu.arff 資料集有209案例，每個案例由6個數值屬性預測1個數值屬性。


 


  
MYCT
  
MMIN
  
MMAX
  
CACH
  
CHMIN
  
CHMAX
  
class
 

  
125
  
256
  
6000
  
256
  
16
  
128
  
198
 

  
29
  
8000
  
32000
  
32
  
8
  
32
  
269
 

  
29
  
8000
  
32000
  
32
  
8
  
32
  
220
 

  
29
  
8000
  
32000
  
32
  
8
  
32
  
172
 

  
29
  
8000
  
16000
  
32
  
8
  
16
  
132
 

  
26
  
8000
  
32000
  
64
  
8
  
32
  
318
 

  
23
  
16000
  
32000
  
64
  
16
  
32
  
367
 

  
23
  
16000
  
32000
  
64
  
16
  
32
  
489
 

  
23
  
16000
  
64000
  
64
  
16
  
32
  
636
 

  
.....
  


  


  


  


  


  


 


參考:
1.weka.classifiers.functions.LinearRegression
   code | doc