weka.classifiers.functions.Logistic

weka.classifiers.functions.Logistic 為羅吉斯迴歸學習器，
建立多類別羅吉斯迴歸模型，含嶺迴歸估計量(ridge estimator)參數，可用來預測類別值。
缺值由ReplaceMissingValuesFilter過濾器補值，文字屬性由NominalToBinaryFilter過濾器轉為數字。
 
參數說明:
 -R <ridge> 設定log相似度的嶺迴歸估計量。預設值1e-8
 -M <number> 設定最大迭代次數。預設值 -1 表示直到收斂為止


> java  weka.classifiers.functions.Logistic  -t data\weather.numeric.arff


Logistic Regression with ridge parameter of 1.0E-8
Coefficients...
                          Class
Variable                    yes
===============================
outlook=sunny           -6.4257
outlook=overcast        13.5922
outlook=rainy           -5.6562
temperature             -0.0776
humidity                -0.1556
windy                    3.7317
Intercept                22.234


Odds Ratios...
                          Class
Variable                    yes
===============================
outlook=sunny            0.0016
outlook=overcast    799848.4279
outlook=rainy            0.0035
temperature              0.9254
humidity                 0.8559
windy                   41.7508


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

=== Error on training data ===

Correctly Classified Instances          11               78.5714 %
Incorrectly Classified Instances         3               21.4286 %
Kappa statistic                          0.5532
Mean absolute error                      0.2066
Root mean squared error                  0.3273
Relative absolute error                 44.4963 %
Root relative squared error             68.2597 %
Total Number of Instances               14     


=== Confusion Matrix ===

 a b   <-- classified as
 7 2 | a = yes
 1 4 | b = no



=== Stratified cross-validation ===

Correctly Classified Instances           8               57.1429 %
Incorrectly Classified Instances         6               42.8571 %
Kappa statistic                          0.0667
Mean absolute error                      0.4548
Root mean squared error                  0.6576
Relative absolute error                 95.5132 %
Root relative squared error            133.2951 %
Total Number of Instances               14     


=== Confusion Matrix ===

 a b   <-- classified as
 6 3 | a = yes
 3 2 | b = no


如下 weather.numeric.arff 案例集的14個案例利用2個文字屬性及2個數字屬性，預測文字屬性。

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:
1.weka.classifiers.functions.Logistic
   code | doc