Which evaluation tool is described as the foundation for classifying outcomes by crossing predicted and actual labels?

Which evaluation tool is described as the foundation for classifying outcomes by crossing predicted and actual labels?

• A. True Positive (TP)
• B. False Positive (FP)
• C. Confusion Matrix
• D. True Negative (TN)

Answer: (C)

A confusion matrix is the framework that crosses predicted labels with actual labels to classify outcomes. It lays out how many predictions fall into each category: true positives, true negatives, false positives, and false negatives. This structure lets you see not just overall accuracy but how the model performs on each class, where it makes mistakes, and how those errors balance between types of misclassification. From the matrix you can derive important metrics like precision, recall, and the F1 score, and you can spot issues such as class imbalance or a tendency to favor predicting one class over the other. For example, in a fraud-detection scenario, you’d count how many fraudulent transactions were correctly flagged (true positives), how many legitimate transactions were flagged (false positives), how many genuine transactions were not flagged (true negatives), and how many fraudulent transactions were missed (false negatives). The other terms—individual cells in this matrix—are outcomes inside the framework, not the tool that organizes and relates predicted and actual labels.

A confusion matrix is the framework that crosses predicted labels with actual labels to classify outcomes. It lays out how many predictions fall into each category: true positives, true negatives, false positives, and false negatives. This structure lets you see not just overall accuracy but how the model performs on each class, where it makes mistakes, and how those errors balance between types of misclassification. From the matrix you can derive important metrics like precision, recall, and the F1 score, and you can spot issues such as class imbalance or a tendency to favor predicting one class over the other. For example, in a fraud-detection scenario, you’d count how many fraudulent transactions were correctly flagged (true positives), how many legitimate transactions were flagged (false positives), how many genuine transactions were not flagged (true negatives), and how many fraudulent transactions were missed (false negatives). The other terms—individual cells in this matrix—are outcomes inside the framework, not the tool that organizes and relates predicted and actual labels.