A confusion matrix is a table that is used to evaluate the performance of a classification model by comparing predicted values against actual values. It is an important tool for understanding the accuracy of a model, and can help identify areas of improvement.
Suppose you are working for a bank and are responsible for assessing loan applications. You have built a machine learning model that predicts whether an applicant is likely to default on their loan or not. The model has been trained on historical data and has achieved an accuracy of 85%.
To evaluate the performance of the model, you can use a confusion matrix. The matrix is constructed by comparing the predicted values against the actual values, as shown below:
Let’s assume that you have 1000 loan applications, out of which 100 are likely to default. When you apply your model to these applications, you get the following results:
- True Positive (TP) — the model correctly predicted that 50 applicants would default on their loan (i.e., 50 out of 100).
- False Positive (FP) — the model predicted that 100 applicants would default on their loan, but in reality, only 50 of them did (i.e., 100–50). TYPE 1 ERROR
- False Negative (FN) — the model predicted that 50…
