A confusion matrix is a table that helps in assessing how good the classification model is. It is used when true values/labels are known. Most beginners in the field of data science feel intimidated by the confusion matrix and think it looks more difficult to comprehend than it really is; let me tell you—it's pretty simple and easy.
Let's understand this by going through an example. Let's say that we have built a classification model that predicts whether a customer would like to buy a certain product or not. To do this, we need to assess the model on unseen data.
There are two classes:
- Yes: The customer will buy the product
- No: The customer will not buy the product
From this, we have put the matrix together:
What are ...
