A confusion matrix is a table that allows you to visualize the performance of a classification algorithm. It is particularly useful in binary classification problems. The matrix consists of four key components: - True Positives (TP): The number of cases where the model correctly predicts the presence of the disease. - True Negatives (TN): The number of cases where the model correctly predicts the absence of the disease. - False Positives (FP): The number of cases where the model incorrectly predicts the presence of the disease. - False Negatives (FN): The number of cases where the model incorrectly predicts the absence of the disease.
