result report

SVM HOG

Accuracy SVM - HOG : 58.02%

Precision Score SVM - HOG: 53.30%

Recall Score SVM - HOG: 100.00%

Classification Report

             precision    recall  f1-score   support

         ev       1.00      0.19      0.33       211
        car       0.53      1.00      0.70       194

avg / total       0.78      0.58      0.50       405

Confusion Matrix
[[ 41 170]
 [  0 194]]
SVM ImagePixel

Accuracy SVM - ImagePixel : 85.19%

Precision Score SVM - ImagePixel: 100.00%

Recall Score SVM - ImagePixel: 69.07%

Classification Report

             precision    recall  f1-score   support

         ev       0.78      1.00      0.88       211
        car       1.00      0.69      0.82       194

avg / total       0.88      0.85      0.85       405

Confusion Matrix
[[211   0]
 [ 60 134]]
SVM Histogram

Accuracy SVM - Histogram : 47.90%

Precision Score SVM - Histogram: 47.90%

Recall Score SVM - Histogram: 100.00%

Classification Report

/usr/local/lib/python2.7/dist-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
             precision    recall  f1-score   support

         ev       0.00      0.00      0.00       211
        car       0.48      1.00      0.65       194

avg / total       0.23      0.48      0.31       405

Confusion Matrix
[[  0 211]
 [  0 194]]
KNN3 Histogram

Accuracy KNN3 - Histogram : 92.84%

Precision Score KNN3 - Histogram: 89.10%

Recall Score KNN3 - Histogram: 96.91%

Classification Report

             precision    recall  f1-score   support

         ev       0.97      0.89      0.93       211
        car       0.89      0.97      0.93       194

avg / total       0.93      0.93      0.93       405

Confusion Matrix
[[188  23]
 [  6 188]]
KNN3 ImagePixel

Accuracy KNN3 - ImagePixel : 94.32%

Precision Score KNN3 - ImagePixel: 100.00%

Recall Score KNN3 - ImagePixel: 88.14%

Classification Report

             precision    recall  f1-score   support

         ev       0.90      1.00      0.95       211
        car       1.00      0.88      0.94       194

avg / total       0.95      0.94      0.94       405

Confusion Matrix
[[211   0]
 [ 23 171]]
KNN3 HOG

Accuracy KNN3 - HOG : 76.79%

Precision Score KNN3 - HOG: 67.48%

Recall Score KNN3 - HOG: 99.48%

Classification Report

             precision    recall  f1-score   support

         ev       0.99      0.56      0.72       211
        car       0.67      0.99      0.80       194

avg / total       0.84      0.77      0.76       405

Confusion Matrix
[[118  93]
 [  1 193]]
MLP HOG

Accuracy MLP - HOG : 95.56%

Precision Score MLP - HOG: 93.56%

Recall Score MLP - HOG: 97.42%

Classification Report

             precision    recall  f1-score   support

         ev       0.98      0.94      0.96       211
        car       0.94      0.97      0.95       194

avg / total       0.96      0.96      0.96       405

Confusion Matrix
[[198  13]
 [  5 189]]
MLP ImagePixel
/usr/local/lib/python2.7/dist-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.
  'precision', 'predicted', average, warn_for)

Accuracy MLP - ImagePixel : 52.10%

Precision Score MLP - ImagePixel: 0.00%

Recall Score MLP - ImagePixel: 0.00%

Classification Report

             precision    recall  f1-score   support

         ev       0.52      1.00      0.69       211
        car       0.00      0.00      0.00       194

avg / total       0.27      0.52      0.36       405

Confusion Matrix
[[211   0]
 [194   0]]
MLP Histogram

Accuracy MLP - Histogram : 97.28%

Precision Score MLP - Histogram: 96.92%

Recall Score MLP - Histogram: 97.42%

Classification Report

             precision    recall  f1-score   support

         ev       0.98      0.97      0.97       211
        car       0.97      0.97      0.97       194

avg / total       0.97      0.97      0.97       405

Confusion Matrix
[[205   6]
 [  5 189]]
GradientBoosting ImagePixel

Accuracy GradientBoosting - ImagePixel : 94.32%

Precision Score GradientBoosting - ImagePixel: 93.85%

Recall Score GradientBoosting - ImagePixel: 94.33%

Classification Report

             precision    recall  f1-score   support

         ev       0.95      0.94      0.95       211
        car       0.94      0.94      0.94       194

avg / total       0.94      0.94      0.94       405

Confusion Matrix
[[199  12]
 [ 11 183]]
GradientBoosting HOG

Accuracy GradientBoosting - HOG : 95.31%

Precision Score GradientBoosting - HOG: 93.10%

Recall Score GradientBoosting - HOG: 97.42%

Classification Report

             precision    recall  f1-score   support

         ev       0.98      0.93      0.95       211
        car       0.93      0.97      0.95       194

avg / total       0.95      0.95      0.95       405

Confusion Matrix
[[197  14]
 [  5 189]]
GradientBoosting Histogram

Accuracy GradientBoosting - Histogram : 98.52%

Precision Score GradientBoosting - Histogram: 97.96%

Recall Score GradientBoosting - Histogram: 98.97%

Classification Report

             precision    recall  f1-score   support

         ev       0.99      0.98      0.99       211
        car       0.98      0.99      0.98       194

avg / total       0.99      0.99      0.99       405

Confusion Matrix
[[207   4]
 [  2 192]]
DecisionTree Histogram

Accuracy DecisionTree - Histogram : 97.28%

Precision Score DecisionTree - Histogram: 96.45%

Recall Score DecisionTree - Histogram: 97.94%

Classification Report

             precision    recall  f1-score   support

         ev       0.98      0.97      0.97       211
        car       0.96      0.98      0.97       194

avg / total       0.97      0.97      0.97       405

Confusion Matrix
[[204   7]
 [  4 190]]
DecisionTree ImagePixel

Accuracy DecisionTree - ImagePixel : 89.88%

Precision Score DecisionTree - ImagePixel: 88.06%

Recall Score DecisionTree - ImagePixel: 91.24%

Classification Report

             precision    recall  f1-score   support

         ev       0.92      0.89      0.90       211
        car       0.88      0.91      0.90       194

avg / total       0.90      0.90      0.90       405

Confusion Matrix
[[187  24]
 [ 17 177]]
DecisionTree HOG

Accuracy DecisionTree - HOG : 89.88%

Precision Score DecisionTree - HOG: 88.06%

Recall Score DecisionTree - HOG: 91.24%

Classification Report

             precision    recall  f1-score   support

         ev       0.92      0.89      0.90       211
        car       0.88      0.91      0.90       194

avg / total       0.90      0.90      0.90       405

Confusion Matrix
[[187  24]
 [ 17 177]]
RandomForrest HOG

Accuracy RandomForrest - HOG : 95.06%

Precision Score RandomForrest - HOG: 96.77%

Recall Score RandomForrest - HOG: 92.78%

Classification Report

             precision    recall  f1-score   support

         ev       0.94      0.97      0.95       211
        car       0.97      0.93      0.95       194

avg / total       0.95      0.95      0.95       405

Confusion Matrix
[[205   6]
 [ 14 180]]
RandomForrest Histogram

Accuracy RandomForrest - Histogram : 98.52%

Precision Score RandomForrest - Histogram: 98.96%

Recall Score RandomForrest - Histogram: 97.94%

Classification Report

             precision    recall  f1-score   support

         ev       0.98      0.99      0.99       211
        car       0.99      0.98      0.98       194

avg / total       0.99      0.99      0.99       405

Confusion Matrix
[[209   2]
 [  4 190]]
RandomForrest ImagePixel

Accuracy RandomForrest - ImagePixel : 95.56%

Precision Score RandomForrest - ImagePixel: 97.83%

Recall Score RandomForrest - ImagePixel: 92.78%

Classification Report

             precision    recall  f1-score   support

         ev       0.94      0.98      0.96       211
        car       0.98      0.93      0.95       194

avg / total       0.96      0.96      0.96       405

Confusion Matrix
[[207   4]
 [ 14 180]]
Adaboost Histogram

Accuracy Adaboost - Histogram : 99.51%

Precision Score Adaboost - Histogram: 99.48%

Recall Score Adaboost - Histogram: 99.48%

Classification Report

             precision    recall  f1-score   support

         ev       1.00      1.00      1.00       211
        car       0.99      0.99      0.99       194

avg / total       1.00      1.00      1.00       405

Confusion Matrix
[[210   1]
 [  1 193]]
Adaboost HOG

Accuracy Adaboost - HOG : 96.30%

Precision Score Adaboost - HOG: 95.90%

Recall Score Adaboost - HOG: 96.39%

Classification Report

             precision    recall  f1-score   support

         ev       0.97      0.96      0.96       211
        car       0.96      0.96      0.96       194

avg / total       0.96      0.96      0.96       405

Confusion Matrix
[[203   8]
 [  7 187]]
Adaboost ImagePixel

Accuracy Adaboost - ImagePixel : 96.54%

Precision Score Adaboost - ImagePixel: 96.88%

Recall Score Adaboost - ImagePixel: 95.88%

Classification Report

             precision    recall  f1-score   support

         ev       0.96      0.97      0.97       211
        car       0.97      0.96      0.96       194

avg / total       0.97      0.97      0.97       405

Confusion Matrix
[[205   6]
 [  8 186]]
ExtraTree Histogram

Accuracy ExtraTree - Histogram : 98.77%

Precision Score ExtraTree - Histogram: 98.96%

Recall Score ExtraTree - Histogram: 98.45%

Classification Report

             precision    recall  f1-score   support

         ev       0.99      0.99      0.99       211
        car       0.99      0.98      0.99       194

avg / total       0.99      0.99      0.99       405

Confusion Matrix
[[209   2]
 [  3 191]]
ExtraTree HOG

Accuracy ExtraTree - HOG : 94.07%

Precision Score ExtraTree - HOG: 96.20%

Recall Score ExtraTree - HOG: 91.24%

Classification Report

             precision    recall  f1-score   support

         ev       0.92      0.97      0.94       211
        car       0.96      0.91      0.94       194

avg / total       0.94      0.94      0.94       405

Confusion Matrix
[[204   7]
 [ 17 177]]
ExtraTree ImagePixel

Accuracy ExtraTree - ImagePixel : 96.79%

Precision Score ExtraTree - ImagePixel: 98.40%

Recall Score ExtraTree - ImagePixel: 94.85%

Classification Report

             precision    recall  f1-score   support

         ev       0.95      0.99      0.97       211
        car       0.98      0.95      0.97       194

avg / total       0.97      0.97      0.97       405

Confusion Matrix
[[208   3]
 [ 10 184]]
XGboost ImagePixel
/usr/local/lib/python2.7/dist-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  if diff:

Accuracy XGboost - ImagePixel : 98.27%

Precision Score XGboost - ImagePixel: 99.47%

Recall Score XGboost - ImagePixel: 96.91%

Classification Report

             precision    recall  f1-score   support

         ev       0.97      1.00      0.98       211
        car       0.99      0.97      0.98       194

avg / total       0.98      0.98      0.98       405

Confusion Matrix
[[210   1]
 [  6 188]]
XGboost HOG
/usr/local/lib/python2.7/dist-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  if diff:

Accuracy XGboost - HOG : 97.53%

Precision Score XGboost - HOG: 97.42%

Recall Score XGboost - HOG: 97.42%

Classification Report

             precision    recall  f1-score   support

         ev       0.98      0.98      0.98       211
        car       0.97      0.97      0.97       194

avg / total       0.98      0.98      0.98       405

Confusion Matrix
[[206   5]
 [  5 189]]
XGboost Histogram
/usr/local/lib/python2.7/dist-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  if diff:

Accuracy XGboost - Histogram : 99.75%

Precision Score XGboost - Histogram: 100.00%

Recall Score XGboost - Histogram: 99.48%

Classification Report

             precision    recall  f1-score   support

         ev       1.00      1.00      1.00       211
        car       1.00      0.99      1.00       194

avg / total       1.00      1.00      1.00       405

Confusion Matrix
[[211   0]
 [  1 193]]