Confusion M

What is Confusion Matrix?

Well, confusion matrix is a technique that helps us determine the performance measurement for machine learning classification problems where output can be two or more classes. It is a table with 4 different combinations of predicted and actual values.

Example of Confusion Matrix:

Below given is an example to know the terms True Positive, True Negative, False Negative, and True Negative.

True Positive:

You projected positive and it turns out to be true. For example, you had predicted that India would win the world cup, and it won.

True Negative:

When you predicted negative, and it is true. You had predicted that England would not win, and it lost.

False Positive:

Your prediction is positive, and it is false.

You had predicted that England would win, but it lost.

False Negative:

Your prediction is negative, and result is also false.

You had predicted that India would not win, but it won.

You should remember that we describe predicted values as either True or False or Positive and Negative.

Among all of these, false positive is the most dangerous one.

HOPE THIS WAS HELPFUL FOR YOU ALL.

THANK YOU!!