SVM v2 result

F_.v1.v2.Test

t c=32.0, g=0.125 CV rate=94.8725
Training...
Output model: F_v1.v2.Train.model
Scaling testing data...
Testing...
Accuracy = 46.9498% (1647/3508) (classification)
Output prediction: F_v1.v2.Test.predict


answer
H, A, S, F, P |Predict
48 69 103 27 264 |0
9 7 2 63 78 |1
0 24 46 3 7 |2
6 7 15 46 77 |3
287 187 184 449 1500 |4

=============
F0_.v1.v2.Test

t c=32.0, g=0.125 CV rate=94.7875
Training...
Output model: F0_v1.v2.Train.model
Scaling testing data...
Testing...
Accuracy = 52.1095% (1828/3508) (classification)
Output prediction: F0_v1.v2.Test.predict


answer
H, A, S, F, P |Predict
47 44 50 1 159 |0
8 2 0 46 56 |1
0 23 72 24 12 |2
1 5 12 26 18 |3
294 220 216 491 1681 |4

J48 v1 result

F_v1.csv(10 CV)
=== Run information ===

Scheme: weka.classifiers.trees.J48 -C 0.25 -M 2
Relation: F_v1
Instances: 6274
Attributes: 103
[list of attributes omitted]
Test mode: 10-fold cross-validation

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

=== Summary ===

Correctly Classified Instances 5823 92.8116 %
Incorrectly Classified Instances 451 7.1884 %
Kappa statistic 0.8832
K&B Relative Info Score 552448.6144 %
K&B Information Score 9940.3112 bits 1.5844 bits/instance
Class complexity | order 0 11285.6028 bits 1.7988 bits/instance
Class complexity | scheme 327256.4897 bits 52.1607 bits/instance
Complexity improvement (Sf) -315970.8869 bits -50.362 bits/instance
Mean absolute error 0.0316
Root mean squared error 0.1657
Relative absolute error 12.8354 %
Root relative squared error 47.2221 %
Total Number of Instances 6274

=== Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure Class
0.923 0.006 0.942 0.923 0.933 _S
0.845 0.012 0.851 0.845 0.848 _A
0.941 0.013 0.925 0.941 0.933 _F
0.861 0.017 0.856 0.861 0.858 _H
0.948 0.07 0.949 0.948 0.949 _P

=== Confusion Matrix ===

a b c d e <-- classified as
553 4 14 1 27 | a = _S
8 382 7 8 47 | b = _A
1 15 859 5 33 | c = _F
1 6 8 570 77 | d = _H
24 42 41 82 3459 | e = _P

Number of Leaves : 278

Size of the tree : 555



================
F0_v1.csv (10 CV)

=== Run information ===

Scheme: weka.classifiers.trees.J48 -C 0.25 -M 2
Relation: F0_v1
Instances: 6274
Attributes: 103
[list of attributes omitted]
Test mode: 10-fold cross-validation

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

Number of Leaves : 262

Size of the tree : 523


Time taken to build model: 34.48 seconds

=== Stratified cross-validation ===
=== Summary ===

Correctly Classified Instances 5742 91.5206 %
Incorrectly Classified Instances 532 8.4794 %
Kappa statistic 0.8615
Mean absolute error 0.0364
Root mean squared error 0.1782
Relative absolute error 14.7968 %
Root relative squared error 50.7918 %
Total Number of Instances 6274

=== Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure Class
0.93 0.008 0.922 0.93 0.926 _S
0.812 0.012 0.838 0.812 0.825 _A
0.92 0.015 0.911 0.92 0.916 _F
0.802 0.016 0.854 0.802 0.827 _H
0.945 0.092 0.935 0.945 0.94 _P

=== Confusion Matrix ===

a b c d e <-- classified as
557 7 10 1 24 | a = _S
9 367 16 8 52 | b = _A
5 12 840 5 51 | c = _F
1 8 8 531 114 | d = _H
32 44 48 77 3447 | e = _P

SVM v1 result

F_v1.test.5

Best c=128.0, g=0.5 CV rate=93.2173
Training...
Output model: F_v1.train.5.model
Scaling testing data...
Testing...
Accuracy = 94.8791% (1334/1406) (classification)
Output prediction: F_v1.test.5.predict


answer
H, A, S, F, P |Predict
129 0 0 1 13 |0
0 88 1 0 2 |1
1 0 124 0 2 |2
0 1 2 188 3 |3
20 11 3 12 805 |4


=========
F0_v1.test.5

Best c=32.0, g=0.5 CV rate=93.0753
Training...
Output model: F0_v1.train.5.model
Scaling testing data...
Testing...
Accuracy = 94.8791% (1334/1406) (classification)
Output prediction: F0_v1.test.5.predict


answer
H, A, S, F, P |Predict
129 0 0 1 12 |0
0 86 1 0 3 |1
1 0 124 0 2 |2
0 0 2 189 2 |3
20 14 3 11 806 |4