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)from sklearn.metrics import confusion_matrix
cm = confusion_matrix(test_Y, predictions_dt)
cm
# after creating the confusion matrix, for better understaning plot the cm.
import seaborn as sn
plt.figure(figsize = (10,8))
# were 'cmap' is used to set the accent colour
sn.heatmap(cm, annot=True, cmap= 'flare', fmt='d', cbar=True)
plt.xlabel('Predicted_Label')
plt.ylabel('Truth_Label')
plt.title('Confusion Matrix - Decision Tree')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)from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_predicted)
cm
# after creating the confusion matrix, for better understaning plot the cm.
import seaborn as sn
plt.figure(figsize = (10,7))
sn.heatmap(cm, annot=True)
plt.xlabel('Predicted')
plt.ylabel('Truth')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 115057df_confusion = pd.crosstab(y_actu, y_pred, rownames=['Actual'], colnames=['Predicted'], margins=True)