Demystifying the Confusion Matrix
A simple 2x2 table can contain insightful metrics that enhance your decision-making.
Understanding the confusion matrix is an important step in statistics, machine learning, or any other field where predictions or classifications are common. The confusion matrix is a type of contingency table with two dimensions that reveal how well a predictive model performs when the outcomes are known. Additionally, when associated costs of incorrect positive and negative guesses differ, the trade-offs can be optimized. Do you know the difference between Sensitivity, Specificity, Recall, Precision, True Positive Rate, and Positive Predictive Value?
