You have a classifier and predictions y_pred for your test set y_test . You want to compute and visualize the confusion matrix using Python. Which of the following options is correct ?

Question

You have a classifier and predictions  y_pred  for your test set  y_test . You want to  compute and visualize the confusion matrix  using Python. Which of the following options is  correct ?

Answer

from sklearn.metrics import confusion_matrix 
 import seaborn as sns 
 cm = confusion_matrix(y_test, y_pred) 
 sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')

Answer

from sklearn.metrics import accuracy_score 
 import seaborn as sns 
 cm = accuracy_score(y_test, y_pred) 
 sns.heatmap(cm, annot=True, fmt='d')

Answer

from sklearn.metrics import confusion_matrix 
 import seaborn as sns 
 cm = confusion_matrix(y_pred, y_test) 
 sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')

Answer

from sklearn.metrics import ConfusionMatrixDisplay 
 cm = ConfusionMatrixDisplay(y_test, y_pred) 
 cm.plot()

Crowdly

You have a classifier and predictions y_pred for your test set y_test . You w...

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