Need for Confusion Matrix in Machine learning

[vishrut-singhal]

• It evaluates the performance of the classification models, when they make predictions on test data, and tells how good our classification model is.
• It not only tells the error made by the classifiers but also the type of errors such as it is either type-I or type-II error.
• With the help of the confusion matrix, we can calculate the different parameters for the model, such as accuracy, precision, etc.

Example : We can understand the confusion matrix using an example.

Suppose we are trying to create a model that can predict the result for the disease that is either a person has that disease or not. So, the confusion matrix for this is given as:

From the above example, we can conclude that:

• The table is given for the two-class classifier, which has two predictions “Yes” and “NO.” Here, Yes defines that patient has the disease, and No defines that patient does not has that disease.
• The classifier has made a total of 100 predictions . Out of 100 predictions, 89 are true predictions , and 11 are incorrect predictions .
• The model has given prediction “yes” for 32 times, and “No” for 68 times. Whereas the actual “Yes” was 27, and actual “No” was 73 times.