strandon prediction using weka, result1

=== Run information ===

Scheme:       weka.classifiers.trees.ADTree -B 10 -E -3
Relation:     predictstrandon2.txt-weka.filters.unsupervised.attribute.Remove-R2,7-weka.filters.unsupervised.attribute.Remove-R14-17-weka.filters.unsupervised.attribute.Remove-R16-17-weka.filters.unsupervised.attribute.Remove-R18
Instances:    61
Attributes:   17
              pos
              res1
              res2
              res3
              res4
              score
              s1
              s3
              s3
              s4
              s5
              s6
              s7
              typen
              total
              relpos
              intrastrandon
Test mode:    evaluate on training data

=== Classifier model (full training set) ===

Alternating decision tree:

: -1.04
|  (1)s6 < 0.291: -1.434
|  (1)s6 >= 0.291: 0.773
|  |  (3)score < 1.401: -0.731
|  |  (3)score >= 1.401: 0.706
|  |  |  (5)s3 < 2.062: 1.42
|  |  |  (5)s3 >= 2.062: -0.452
|  (2)relpos < 6.5: -0.737
|  |  (4)res3 = T: 0.427
|  |  (4)res3 != T: -0.819
|  (2)relpos >= 6.5: 0.658
|  (6)res1 = A: 0.254
|  (6)res1 != A: -0.452
|  |  (7)pos < 54: 0.146
|  |  (7)pos >= 54: -0.418
|  |  |  (8)typen = &12: 0.119
|  |  |  (8)typen != &12: -0.378
Legend: -ve = FALSE, +ve = TRUE
Tree size (total number of nodes): 25
Leaves (number of predictor nodes): 17

Time taken to build model: 0.02 seconds

=== Evaluation on training set ===
=== Summary ===

Correctly Classified Instances          61              100      %
Incorrectly Classified Instances         0                0      %
Kappa statistic                          1    
Mean absolute error                      0.0237
Root mean squared error                  0.0445
Relative absolute error                 12.6362 %
Root relative squared error             14.9366 %
Total Number of Instances               61    

=== Detailed Accuracy By Class ===

TP Rate   FP Rate   Precision   Recall  F-Measure   Class
  1         0          1         1         1        FALSE
  1         0          1         1         1        TRUE

=== Confusion Matrix ===

  a  b   <-- classified as
 55  0   a = FALSE
  0  6   b = TRUE