Table 1.

Binary classification results. Confusion matrix can visualize and evaluate the performance of classification models. Rows represent the instances in predicted classes while columns represent the instances in real classes. True positive and true negative refer to the results where the model correctly predicts the positive and negative classes, respectively. Similarly, false positive and false negative refer to the results where the model incorrectly predicts the positive and negative classes, respectively.

	Real Positive	Real Negative
Predicted Positive	True Positive (TP)	False Negative (FN)
Predicted Negative	False Positive (FP)	True Negative (TN)