# Cyber crime cases where they talk about confusion matrix or its two types of error.

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I have written this article for the solution of Cyber crime cases where they talk about confusion matrix or its two types of error.

# What is a Confusion Matrix?

The million dollar question — what, after all, is a confusion matrix?

A Confusion matrix is an N x N matrix used for evaluating the performance of a classification model, where N is the number of target classes. The matrix compares the actual target values with those predicted by the machine learning model. This gives us a holistic view of how well our classification model is performing and what kinds of errors it is making.

# Cybercrime

Cybercrime, or computer crime, is a crime that involves a computer and a network. The computer may have been used in the commission of a crime, or it may be the target. Cybercrime may harm someone’s security and financial health.

Reference :- https://en.wikipedia.org/wiki/Cybercrime

# Why Do We Need a Confusion Matrix?

Before we answer this question, let’s think about a hypothetical classification problem.

Let’s say you want to predict how many people are infected with a contagious virus in times before they show the symptoms, and isolate them from the healthy population (ringing any bells, yet?

). The two values for our target variable would be: Sick and Not Sick.

Now, you must be wondering — why do we need a confusion matrix when we have our all-weather friend — Accuracy? Well, let’s see where accuracy falters.

# Confusion Matrix for Multi-Class Classification

How would a confusion matrix work for a multi-class classification problem? Well, don’t scratch your head! We will have a look at that here.

Let’s draw a confusion matrix for a multiclass problem where we have to predict whether a person loves Facebook, Instagram or Snapchat. The confusion matrix would be a 3 x 3 matrix like this

# Understanding True Positive, True Negative, False Positive and False Negative in a Confusion Matrix

True Positive (TP)

• The predicted value matches the actual value
• The actual value was positive and the model predicted a positive value

True Negative (TN)

• The predicted value matches the actual value
• The actual value was negative and the model predicted a negative value

False Positive (FP) — Type 1 error

• The predicted value was falsely predicted
• The actual value was negative but the model predicted a positive value
• Also known as the Type 1 error

False Negative (FN) — Type 2 error

• The predicted value was falsely predicted
• The actual value was positive but the model predicted a negative value
• Also known as the Type 2 error

Let me give you an example to better understand this. Suppose we had a classification dataset with 1000 data points. We fit a classifier on it and get the below confusion matrix

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