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‎log/QP-5-over-7.log

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‎log/QP-Linear-1-over-7.log

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‎log/QP-RBF-1-over-7.log

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‎log/SKLEARN-Linear-1-over-7.log

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/usr/bin/python2.7 /home/soloice/PycharmProjects/SVM_RC/src2/svm_test_full.py
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<type 'numpy.ndarray'>
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shape (full): (70000, 784) (70000,)
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(10000, 784) Counter({1.0: 1134, 7.0: 1044, 2.0: 1023, 3.0: 1018, 8.0: 1015, 6.0: 1006, 0.0: 974, 9.0: 967, 4.0: 966, 5.0: 853})
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(60000, 784) Counter({1.0: 6743, 7.0: 6249, 3.0: 6123, 9.0: 5991, 2.0: 5967, 0.0: 5929, 6.0: 5870, 4.0: 5858, 8.0: 5810, 5.0: 5460})
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(10000, 784) (10000,)
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Total Training time: 12.1865880489
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Total Predicting time: 117.722342968
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predicted classes: [ 9. 0. 3. ..., 9. 2. 7.]
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Classification report for classifier SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
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decision_function_shape='ovo', degree=3, gamma='auto', kernel='linear',
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max_iter=-1, probability=False, random_state=None, shrinking=True,
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tol=0.001, verbose=False):
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precision recall f1-score support
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0.0 0.94 0.98 0.96 5929
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1.0 0.94 0.98 0.96 6743
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2.0 0.89 0.91 0.90 5967
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3.0 0.89 0.89 0.89 6123
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4.0 0.91 0.92 0.92 5858
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5.0 0.88 0.86 0.87 5460
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6.0 0.95 0.94 0.95 5870
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7.0 0.93 0.93 0.93 6249
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8.0 0.91 0.86 0.88 5810
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9.0 0.91 0.88 0.89 5991
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avg / total 0.92 0.92 0.92 60000
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Confusion matrix:
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[[5781 1 25 8 6 43 37 2 20 6]
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[ 1 6590 25 24 4 10 2 18 63 6]
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[ 62 87 5412 88 60 16 50 72 106 14]
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[ 28 42 134 5462 15 229 15 53 103 42]
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[ 25 23 86 6 5412 8 34 36 15 213]
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[ 73 41 57 256 47 4715 84 17 135 35]
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[ 52 13 92 3 54 93 5543 3 17 0]
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[ 16 27 56 55 79 16 1 5833 8 158]
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[ 54 117 113 180 41 170 55 38 4979 63]
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[ 49 39 53 49 238 41 3 217 52 5250]]
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For training set!
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Classification report for classifier SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
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decision_function_shape='ovo', degree=3, gamma='auto', kernel='linear',
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max_iter=-1, probability=False, random_state=None, shrinking=True,
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tol=0.001, verbose=False):
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precision recall f1-score support
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0.0 1.00 1.00 1.00 974
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1.0 1.00 1.00 1.00 1134
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2.0 1.00 1.00 1.00 1023
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3.0 1.00 1.00 1.00 1018
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4.0 1.00 1.00 1.00 966
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5.0 1.00 1.00 1.00 853
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6.0 1.00 1.00 1.00 1006
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7.0 0.99 0.99 0.99 1044
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8.0 1.00 1.00 1.00 1015
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9.0 0.99 0.99 0.99 967
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avg / total 1.00 1.00 1.00 10000
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Confusion matrix:
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[[ 974 0 0 0 0 0 0 0 0 0]
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[ 0 1134 0 0 0 0 0 0 0 0]
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[ 0 0 1023 0 0 0 0 0 0 0]
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[ 0 0 0 1015 0 2 0 0 1 0]
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[ 0 0 0 0 964 0 0 0 0 2]
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[ 0 1 0 1 0 851 0 0 0 0]
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[ 0 0 0 0 0 0 1006 0 0 0]
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[ 0 0 0 1 0 0 0 1037 0 6]
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[ 0 2 0 1 0 1 0 0 1011 0]
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[ 0 0 0 0 1 0 0 10 0 956]]
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Process finished with exit code 0

‎log/SKLEARN-RBF-1-over-7.log

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/usr/bin/python2.7 /home/soloice/PycharmProjects/SVM_RC/src2/svm_test_full.py
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<type 'numpy.ndarray'>
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shape (full): (70000, 784) (70000,)
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(10000, 784) Counter({1.0: 1098, 2.0: 1043, 7.0: 1040, 9.0: 1037, 6.0: 1024, 3.0: 1014, 0.0: 978, 8.0: 972, 4.0: 927, 5.0: 867})
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(60000, 784) Counter({1.0: 6779, 7.0: 6253, 3.0: 6127, 2.0: 5947, 0.0: 5925, 9.0: 5921, 4.0: 5897, 8.0: 5853, 6.0: 5852, 5.0: 5446})
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(10000, 784) (10000,)
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Total Training time: 30.2058560848
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Total Predicting time: 231.573395014
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predicted classes: [ 2. 4. 9. ..., 3. 4. 9.]
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Classification report for classifier SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
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decision_function_shape='ovo', degree=3, gamma='auto', kernel='rbf',
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max_iter=-1, probability=False, random_state=None, shrinking=True,
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tol=0.001, verbose=False):
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precision recall f1-score support
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0.0 0.95 0.97 0.96 5925
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1.0 0.92 0.98 0.95 6779
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2.0 0.92 0.90 0.91 5947
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3.0 0.91 0.88 0.89 6127
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4.0 0.91 0.93 0.92 5897
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5.0 0.88 0.87 0.88 5446
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6.0 0.94 0.95 0.95 5852
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7.0 0.94 0.92 0.93 6253
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8.0 0.91 0.87 0.89 5853
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9.0 0.89 0.89 0.89 5921
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avg / total 0.92 0.92 0.92 60000
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Confusion matrix:
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[[5737 1 19 19 14 41 38 4 45 7]
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[ 1 6632 34 18 8 25 7 12 34 8]
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[ 46 54 5339 64 100 20 87 72 139 26]
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[ 19 57 122 5416 8 231 26 63 135 50]
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[ 15 25 35 0 5482 4 45 13 18 260]
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[ 62 92 31 230 48 4762 105 11 67 38]
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[ 42 30 49 3 34 85 5586 0 23 0]
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[ 17 87 86 10 87 11 1 5727 18 209]
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[ 36 167 61 137 47 173 50 27 5073 82]
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[ 44 42 27 86 203 40 4 151 41 5283]]
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For training set!
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Classification report for classifier SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
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decision_function_shape='ovo', degree=3, gamma='auto', kernel='rbf',
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max_iter=-1, probability=False, random_state=None, shrinking=True,
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tol=0.001, verbose=False):
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precision recall f1-score support
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0.0 0.96 0.98 0.97 978
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1.0 0.93 0.98 0.96 1098
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2.0 0.94 0.91 0.92 1043
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3.0 0.93 0.88 0.91 1014
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4.0 0.91 0.94 0.92 927
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5.0 0.89 0.91 0.90 867
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6.0 0.94 0.97 0.95 1024
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7.0 0.94 0.92 0.93 1040
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8.0 0.92 0.88 0.90 972
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9.0 0.90 0.91 0.91 1037
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avg / total 0.93 0.93 0.93 10000
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Confusion matrix:
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[[ 956 0 2 2 2 4 6 2 4 0]
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[ 0 1077 5 4 0 4 2 0 4 2]
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[ 12 4 947 10 16 2 18 11 21 2]
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[ 4 7 19 896 1 45 5 9 20 8]
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[ 1 4 3 0 870 0 6 3 2 38]
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[ 5 13 6 20 6 791 13 2 7 4]
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[ 5 5 5 0 7 9 989 0 4 0]
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[ 0 18 9 0 17 1 0 957 2 36]
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[ 4 24 12 10 10 27 8 4 860 13]
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[ 4 3 3 17 30 4 1 25 7 943]]
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Process finished with exit code 0

‎log/SKLEARN-RBF-6-over-7.log

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/usr/bin/python2.7 /home/soloice/PycharmProjects/SVM_RC/src2/svm_test_full.py
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<type 'numpy.ndarray'>
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shape (full): (70000, 784) (70000,)
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(60000, 784) Counter({1.0: 6756, 7.0: 6265, 3.0: 6129, 2.0: 5979, 6.0: 5964, 9.0: 5954, 0.0: 5936, 8.0: 5855, 4.0: 5801, 5.0: 5361})
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(10000, 784) Counter({1.0: 1121, 7.0: 1028, 4.0: 1023, 3.0: 1012, 2.0: 1011, 9.0: 1004, 8.0: 970, 0.0: 967, 5.0: 952, 6.0: 912})
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(60000, 784) (60000,)
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Total Training time: 513.751617193
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Total Predicting time: 138.237872839
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predicted classes: [ 6. 4. 0. ..., 4. 3. 6.]
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Classification report for classifier SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
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decision_function_shape='ovo', degree=3, gamma='auto', kernel='rbf',
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max_iter=-1, probability=False, random_state=None, shrinking=True,
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tol=0.001, verbose=False):
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precision recall f1-score support
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0.0 0.97 0.98 0.97 967
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1.0 0.96 0.98 0.97 1121
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2.0 0.94 0.93 0.93 1011
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3.0 0.92 0.92 0.92 1012
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4.0 0.93 0.96 0.94 1023
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5.0 0.92 0.93 0.92 952
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6.0 0.96 0.97 0.96 912
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7.0 0.95 0.94 0.95 1028
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8.0 0.94 0.91 0.92 970
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9.0 0.93 0.90 0.91 1004
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avg / total 0.94 0.94 0.94 10000
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Confusion matrix:
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[[ 943 0 3 2 2 6 4 1 6 0]
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[ 0 1103 3 6 3 0 0 2 4 0]
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[ 5 4 938 10 14 5 8 11 14 2]
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[ 0 5 18 932 1 32 1 9 10 4]
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[ 2 2 2 0 980 1 4 0 2 30]
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[ 2 7 7 22 4 881 17 0 10 2]
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[ 5 2 4 0 4 5 887 0 5 0]
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[ 0 7 11 2 11 2 0 968 2 25]
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[ 6 17 9 20 7 20 4 2 881 4]
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[ 8 5 4 15 30 9 3 24 5 901]]
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For training set!
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Classification report for classifier SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
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decision_function_shape='ovo', degree=3, gamma='auto', kernel='rbf',
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max_iter=-1, probability=False, random_state=None, shrinking=True,
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tol=0.001, verbose=False):
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precision recall f1-score support
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0.0 0.97 0.98 0.98 5936
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1.0 0.96 0.98 0.97 6756
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2.0 0.94 0.93 0.94 5979
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3.0 0.93 0.92 0.93 6129
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4.0 0.93 0.95 0.94 5801
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5.0 0.92 0.92 0.92 5361
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6.0 0.96 0.97 0.96 5964
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7.0 0.96 0.94 0.95 6265
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8.0 0.94 0.91 0.93 5855
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9.0 0.93 0.92 0.92 5954
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avg / total 0.94 0.94 0.94 60000
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Confusion matrix:
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[[5828 1 16 9 11 22 19 3 23 4]
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[ 2 6632 37 16 7 15 5 7 25 10]
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[ 31 24 5587 44 73 16 67 46 77 14]
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[ 14 28 106 5637 7 154 21 53 79 30]
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[ 10 17 34 0 5530 4 32 13 9 152]
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[ 33 48 22 138 36 4922 83 9 52 18]
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[ 36 16 23 1 28 58 5788 0 14 0]
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[ 14 44 64 17 69 12 1 5898 13 133]
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[ 17 96 49 91 34 119 39 24 5339 47]
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[ 24 23 24 84 165 18 1 121 39 5455]]
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Process finished with exit code 0

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