Confusion Matrix
Definition
A confusion matrix is a table used to evaluate the performance of a classification model by summarizing predicted versus actual class outcomes.
This table displays the number of correct and incorrect predictions made by a model when compared against the true values. Each row typically represents the instances in an actual class, while each column represents the instances in a predicted class. The four key components often included are true positives (correctly predicted positive cases), true negatives (correctly predicted negative cases), false positives (incorrectly predicted positive cases), and false negatives (incorrectly predicted negative cases).
For instance, a confusion matrix could illustrate how many financial transactions were correctly identified as fraudulent versus how many legitimate transactions were mistakenly flagged. This tool is commonly employed in machine learning, particularly within supervised learning tasks, to derive metrics like accuracy, precision, recall, and F1-score, providing a comprehensive view of a classification algorithm's effectiveness.