## Introduction

In **Machine Learning,** one essential step is evaluating the performance of a model. For classification models, the **Confusion Matrix** serves as a fundamental instrument for evaluating the performance. It provides a clear and visual summary of the prediction accuracy of a model by illustrating the correspondence between the predicted and actual classes. This visualization of the results of a machine learning model enables Data Scientists and Data Analysts to make decisions about model refinement and optimization. In this tutorial, we will explore the **Confusion Matrix** and explain its components and interpretation. We also look at a hands-on example of the **Confusion Matrix**.

## What is a Confusion Matrix?

A Confusion Matrix can be used to evaluate the performance of classification models in **Machine Learning**. It consists of rows and columns representing the predicted and actual classes. The **Confusion Matrix** can have different dimensions based on the number of classes in the classification problem.

In the following, we assume a Machine Learning Model for **Binary Classification**. In this case there are two classes, so the **Confusion Matrix **is a 2x2 Matrix. The rows represent the predicted classes and the columns represent the actual classes.

The **Confusion Matrix** is shown below:

Here is an explanation of the terms in the **Confusion Matrix**:

**True Positive (TP):**The predicted class is positive and it's true.**True Negative (TN):**The predicted class is negative and it's true.**False Positive (FP):**The predicted class is positive but it's false. This is known as Type I Error.**False Negative (FN):**The predicted class is negative but it's false. This is known as Type II Error:

## Hands-On Example

Now, let's look at a hands-on example of a **Binary Classification **problem where the results of the model should be visualized with a **Confusion Matrix**.

Suppose we have developed a Machine Learning model to classify images as cats or as dogs. In this example, we have the following two classes:

**Positive:**Cat**Negative:**Dog

After training the model, we apply it to a set of test images to evaluate its performance. We have a total number of **40 test images**,** **of which 26 are cats and 14 are dogs.

The **Confusion Matrix** for this classification task would look something like this:

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