What is confusion matrix and why it is used?

Well, it is a performance measurement for machine learning classification problem where output can be two or more classes. It is a table with 4 different combinations of predicted and actual values.

• TP: True Positive: Predicted values correctly predicted as actual positive
• FP: Predicted values incorrectly predicted an actual positive. i.e., Negative values predicted as positive
• FN: False Negative: Positive values predicted as negative
• TN: True Negative: Predicted values correctly predicted as an actual negative

Example of Confusion Matrix:

Confusion Matrix is a useful machine learning method which allows you to measure Recall, Precision, Accuracy, and AUC-ROC curve. Below given is an example to know the terms True Positive, True Negative, False Negative, and True Negative.

True Positive:

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

True Negative:

When you predicted negative, and it’s 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 it is also false.

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

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

Why you need Confusion matrix?

Here are pros/benefits of using a confusion matrix.

• It shows how any classification model is confused when it makes predictions.
• Confusion matrix not only gives you insight into the errors being made by your classifier but also types of errors that are being made.
• This breakdown helps you to overcomes the limitation of using classification accuracy alone.
• Every column of the confusion matrix represents the instances of that predicted class.
• Each row of the confusion matrix represents the instances of the actual class.
• It provides insight not only the errors which are made by a classifier but also errors that are being made.

Lets take one more example :: —

Let’s understand TP, FP, FN, TN in terms of pregnancy analogy.

True Positive:

Interpretation: You predicted positive and it’s true.

You predicted that a woman is pregnant and she actually is.

True Negative:

Interpretation: You predicted negative and it’s true.

You predicted that a man is not pregnant and he actually is not.

False Positive: (Type 1 Error)

Interpretation: You predicted positive and it’s false.

You predicted that a man is pregnant but he actually is not.

False Negative: (Type 2 Error)

Interpretation: You predicted negative and it’s false.

You predicted that a woman is not pregnant but she actually is.

Just Remember, We describe predicted values as Positive and Negative and actual values as True and False.

What is cyber security?

Cybersecurity is the practice of protecting systems, networks, and programs from digital attacks. These cyberattacks are usually aimed at accessing, changing, or destroying sensitive information; extorting money from users; or interrupting normal business processes.

How confusion Matrix and Cyber security are related?

Most of the cyber crimes takes place due to Type 1 error. Any Hacker tries to break our system and gets it, but our system still show everything positive. In reality it is negative but due to type 1 error it is showing positive. So this can result in loss of personal data. That’s why type 1 error is most dangerous error.