Confusion Matrix

What is a Confusion Matrix?

A confusion matrix is a table that summarizes the performance of a machine learning model by comparing its predicted output with the actual output. A confusion matrix shows the number of true positives, true negatives, false positives, and false negatives for each class in the data. It can be used to calculate several evaluation metrics such as accuracy, precision, recall, and F1 score.

What does a Confusion Matrix do?

A confusion matrix helps to evaluate the performance of a machine learning model by summarizing its predicted output and comparing it to the actual output:

  • True positives (TP): The number of data points that were correctly classified as positive.

  • True negatives (TN): The number of data points that were correctly classified as negative.

  • False positives (FP): The number of data points that were incorrectly classified as positive.

  • False negatives (FN): The number of data points that were incorrectly classified as negative.

Some benefits of using a Confusion Matrix

A confusion matrix offers several benefits for evaluating the performance of a machine learning model:

  • Performance evaluation: A confusion matrix provides a detailed summary of the performance of a model, including the number of true and false predictions for each class.

  • Evaluation metrics: A confusion matrix can be used to calculate several evaluation metrics such as accuracy, precision, recall, and F1 score.

  • Model improvement: A confusion matrix can help identify areas where a model is making errors and can be used to improve the model’s performance.

More resources to learn more about Confusion Matrices

To learn more about confusion matrices and their applications, you can explore the following resources: