Hello Viabyte! In the world of machine learning, confusion matrix is one of the most important evaluation tools. It helps to evaluate the performance of a machine learning algorithm by comparing the predicted results with the actual results. In this article, we will discuss the concept of confusion matrix, its components, and how it can be used to evaluate the performance of a machine learning algorithm.
What is a Confusion Matrix?
A confusion matrix is a table that is used to evaluate the performance of a classification model. It consists of four components: true positive, true negative, false positive, and false negative. These components help in determining the accuracy, precision, recall, and F1 score of a model.
Components of Confusion Matrix
Let’s understand the components of a confusion matrix:
- True Positive (TP) – The number of correct positive predictions made by the model.
- True Negative (TN) – The number of correct negative predictions made by the model.
- False Positive (FP) – The number of incorrect positive predictions made by the model.
- False Negative (FN) – The number of incorrect negative predictions made by the model.
How to Create a Confusion Matrix?
To create a confusion matrix, you need to have actual and predicted values. Let’s consider the example of a binary classification problem where we need to predict whether a customer will buy a product or not. We have the following actual and predicted values:
