Confusion Matrix

A confusion matrix is a table used to evaluate the performance of a classification model. It shows the number of correct and incorrect predictions made by the model compared to the actual outcomes. The matrix is divided into four quadrants: true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). True positives are the cases where the model correctly predicts the positive class, false positives are the cases where the model predicts the positive class but the actual class is negative, true negatives are the cases where the model correctly predicts the negative class, and false negatives are the cases where the model predicts the negative class but the actual class is positive. The confusion matrix is useful for calculating various performance metrics such as accuracy, precision, recall, and F1 score.
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