machine learning - How to prepare confusion matrix from the predicted class probabilities?

Question

There is a Naive Bayesian classifier which is created with a given training data. In the table, the predicted positive class probabilities and the actual class labels are shown. I want to prepare the confusion matrix but I could not find out how to do it with just knowing the probabilities.

ID	Actual class label	Predicted positive class probability
1	+	0.6
2	+	0.8
3	-	0.2
4	+	0.3
5	-	0.4

question from:https://stackoverflow.com/questions/65927584/how-to-prepare-confusion-matrix-from-the-predicted-class-probabilities

Answer 1

First, you need to have discrete class labels to compute confusion matrix. Define a threshold on the predicted positive class probability to predict class labels (y_pred). You can then use actual class labels (y_actual) and y_pred to compute the confusion matrix.

from sklearn.metrics import confusion_matrix
confusion_matrix(y_actual, y_pred)