PYTHON

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)

sklearn plot confusion matrix

import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, plot_confusion_matrix

clf = # define your classifier (Decision Tree, Random Forest etc.)
clf.fit(X, y) # fit your classifier

# make predictions with your classifier
y_pred = clf.predict(X)         
# optional: get true negative (tn), false positive (fp)
# false negative (fn) and true positive (tp) from confusion matrix
M = confusion_matrix(y, y_pred)
tn, fp, fn, tp = M.ravel() 
# plotting the confusion matrix
plot_confusion_matrix(clf, X, y)
plt.show()

python plot_confusion_matrix

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')

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)

confusion matrix python code

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')

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

sklearn plot confusion matrix

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import  plot_confusion_matrix
clf = LogisticRegression()
clf.fit(X_train,y_train)
disp = plot_confusion_matrix(clf,X_test,y_test,cmap="Blues",values_format='.3g')
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')

plot confusion matrix scikit learn

from sklearn import metrics
metrics.ConfusionMatrixDisplay.from_predictions(true_y, predicted_y).plot()

how to plot confusion matrix

import seaborn as sns
from sklearn.metrics import confusion_matrix
# y_test  : actual labels or target
# y_preds : predicted labels or target
sns.heatmap(confusion_matrix(y_test, y_preds),annot=True);

how to get confusion matrix in python

from sklearn.metrics import confusion_matrix
conf_mat = confusion_matrix(y_test, y_pred)

plotting confusion matrix

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

confusion matrix python

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