# What is Confusion Matrix in Machine Learning

Confusion matrix is an indispensable tool in machine learning domain, specifically in classification tasks.

Furthermore, in this article, I’ll explain what a confusion matrix is, why we need it, and how to interpret it.

## What is a Confusion Matrix and Why Do We Need It in Machine Learning?

A confusion matrix is a table that helps visualize the performance of a classification algorithm.

Moreover, it compares the actual class labels with the predicted class labels, which enables us to assess the accuracy of our models.

The reason why we call it a confusion matrix is because it highlights where the model is “confused” or making mistakes.

In other words, a confusion matrix is a summary of the correct and incorrect predictions a classification model made.

Furthermore, it’s purpose is to quantify the model’s performance in terms of false positives, false negatives, true positives, and true negatives.

### The 4 Quadrants of Confusion Matrix

The confusion matrix is organized into the following four quadrants:

**Top-left: True Positives (TP)**– Correctly predicted positive instances.**Top-right: False Positives (FP)**– Incorrectly predicted positive instances (Type I error).**Bottom-left: False Negatives (FN)**– Incorrectly predicted negative instances (Type II error).**Bottom-right: True Negatives (TN)**– Correctly predicted negative instances.

### How to Solve a Confusion Matrix in Machine Learning

We can solve a confusion matrix by following the steps below:

- Collect the actual class labels and the predicted class labels from the model.
- Create a matrix with rows representing actual classes and columns representing predicted classes.
- Fill in the matrix with counts of TP, TN, FP, and FN.
- Analyze the matrix to assess the model’s performance and identify areas for improvement.

### What is Confusion Matrix Good For?

The confusion matrix is beneficial because it:

- Provides a detailed summary of classification performance.
- Helps calculate various performance metrics like accuracy, precision, recall, and F1-score.
- Facilitates the identification of specific types of errors made by the model.

## How to Interpret Confusion Matrix in CNN

To interpret a confusion matrix in a convolutional neural network (CNN) or any classification model there are 3 steps we need to follow:

**Calculate performance metrics**like accuracy, precision, recall, and F1-score using the values in the confusion matrix.**Analyze the matrix**to identify any patterns or trends in errors made by the model.**Use the insights**gained to fine-tune the model’s hyperparameters or improve the dataset.

## Conclusion

To conclude, a confusion matrix is an essential tool for evaluating the performance of classification models in machine learning.

Moreover, by understanding how to create, interpret, and analyze a confusion matrix, we can fine-tune our models to make better predictions and ultimately, deliver more value from our machine learning projects.