What is a confusion matrix?

A confusion matrix is a table used in machine learning and statistical classification to evaluate the performance of a model. It summarizes the actual and predicted classifications of a model’s predictions in a tabular format.

The confusion matrix is a 2X2 table that contains 4 outputs provided by the binary classifier.

• True Positive (TP): The model correctly predicted a positive classification (e.g., correctly identified a patient as having a disease).
• False Positive (FP): The model incorrectly predicted a positive classification (e.g., falsely identified a healthy patient as having a disease).
• True Negative (TN): The model correctly predicted a negative classification (e.g., correctly identified a healthy patient as not having a disease).
• False Negative (FN): The model incorrectly predicted a negative classification (e.g., falsely identified a patient with a disease as healthy).

The confusion matrix provides a summary of the model’s performance, including metrics such as accuracy, precision, recall, and F1 score. These metrics can be used to evaluate the strengths and weaknesses of a model and guide improvements.

Precision

Recall

F1 Score

The confusion matrix provides a summary of the model’s performance, including metrics such as accuracy, precision, recall, and F1 score. These metrics can be used to evaluate the strengths and weaknesses of a model and guide improvements.