Confusion matrix python

from sklearn.metrics import confusion_matrix
conf_mat = confusion_matrix(y_test, y_pred)
sns.heatmap(conf_mat, square=True, annot=True, cmap='Blues', fmt='d', cbar=False)

Source: grepper.com

Confusion matrix python

By definition, entry i,j in a confusion matrix is the number of 
observations actually in group i, but predicted to be in group j. 
Scikit-Learn provides a confusion_matrix function:

from sklearn.metrics import confusion_matrix
y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
confusion_matrix(y_actu, y_pred)
# Output
# array([[3, 0, 0],
#        [0, 1, 2],
#        [2, 1, 3]], dtype=int64)

Source: scikit-learn.org

Confusion matrix python

df_confusion = pd.crosstab(y_actu, y_pred, rownames=['Actual'], colnames=['Predicted'], margins=True)

Source: stackoverflow.com

Confusion matrix python

from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report, confusion_matrix

print(confusion_matrix(y_test, y_pred_test.round()))
print(classification_report(y_test, y_pred_test.round()))

# Output:
[[99450   250]
 [ 4165 11192]]
              precision    recall  f1-score   support

           0       0.96      1.00      0.98     99700
           1       0.98      0.73      0.84     15357

    accuracy                           0.96    115057
   macro avg       0.97      0.86      0.91    115057
weighted avg       0.96      0.96      0.96    115057

Source: localhost

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