alyato/TEST_SVM_ON_VIPER.md

## TEST_SVM_ON_VIPER.md

      
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              TEST_SVM_ON_VIPER.md
            
          
    Training phase

20000 training instances
The number of support vectors is around 15000, which means that most of the training data are near the separating hyper-plane.
The training accuracy is around 80%
Test on full data


Accuracy = 52.3734% (662/1264) (classification)
Accuracy = 62.5791% (791/1264) (classification)
Accuracy = 61.0759% (772/1264) (classification)
Accuracy = 60.9968% (771/1264) (classification)
Accuracy = 63.5285% (803/1264) (classification)
Accuracy = 61.9462% (783/1264) (classification)
Accuracy = 57.1203% (722/1264) (classification)
Accuracy = 50% (632/1264) (classification)

Test on positive data


Accuracy = 69.7785% (441/632) (classification)
Accuracy = 92.5633% (585/632) (classification)
Accuracy = 91.9304% (581/632) (classification)
Accuracy = 91.6139% (579/632) (classification)
Accuracy = 87.3418% (552/632) (classification)
Accuracy = 79.7468% (504/632) (classification)
Accuracy = 84.6519% (535/632) (classification)
Accuracy = 88.9241% (562/632) (classification)

Test on negative data


Accuracy = 34.9684% (221/632) (classification)
Accuracy = 32.5949% (206/632) (classification)
Accuracy = 30.2215% (191/632) (classification)
Accuracy = 30.3797% (192/632) (classification)
Accuracy = 39.7152% (251/632) (classification)
Accuracy = 44.1456% (279/632) (classification)
Accuracy = 29.5886% (187/632) (classification)
Accuracy = 11.0759% (70/632) (classification)

分析

训练数据接近3/4都是支持向量，说明了数据本身的可分性不强，训练的结果更像是over-fitting的结果。
测试结果很差，且模型倾向于将结果分为正类别。
另外，可以注意到第8块的结果最差，可能包含的信息很少。