Confusion matrix is used to explain a model’s performance and gives the summary of predictions on the classification problems. It assists in identifying the uncertainty between classes.
A confusion matrix gives the count of correct and incorrect values and also the error types.Accuracy of the model:
Accuracy of the model
For example, consider this confusion matrix. It consists of values as True Positive, True Negative, False Positive, and False Negative for a classification model. Now, the accuracy of the model can be calculated as follows:
What is a Confusion Matrix
Thus, in our example:
Accuracy = (200 + 50) / (200 + 50 + 10 + 60) = 0.78
This means that the model’s accuracy is 0.78, corresponding to its True Positive, True Negative, False Positive, and False Negative values.
