What does a confusion matrix illustrate in a machine learning model?

A confusion matrix is a key tool used in the evaluation of the performance of a classification model in machine learning. This matrix summarizes the results of predictions made by the model by providing a detailed breakdown of its performance on test data. Specifically, it illustrates the occurrences of true positives, true negatives, false positives, and false negatives.

The reason why the correct choice is significant is that false positives and false negatives are critical for understanding the accuracy of a model’s predictions.

• False positives occur when the model predicts a positive outcome where the actual outcome is negative. This metric is important in contexts where incorrectly predicting a positive outcome has a major consequence, such as identifying diseases in medical diagnoses.

• False negatives, on the other hand, occur when the model fails to identify a positive condition that is present, which can also have serious implications, especially in critical areas like safety and compliance.

By analyzing the occurrences of these two factors, practitioners can assess the model’s effectiveness and make necessary adjustments to improve its predictive capability. It supports a better understanding of where the model is making errors, which is essential for making improvements in model training and feature selection. Thus, the emphasis on false positives and false negatives within the confusion matrix provides valuable insights that are critical for improving machine