This segment highlights the shortcomings of using accuracy as the sole evaluation metric in binary classification, particularly when dealing with imbalanced datasets or situations where false positives and false negatives have different levels of importance. It introduces the need for alternative metrics to provide a more comprehensive assessment of model performance. Binary classification metrics beyond accuracy include precision (true positives / predicted positives), recall/sensitivity (true positives / actual positives), and specificity (true negatives / actual negatives). These address situations where false positives and negatives have unequal importance or datasets are imbalanced. The ROC curve visualizes the trade-off between true positive rate and false positive rate, aiding model selection based on application needs. This segment uses a visual representation from Wikipedia to explain the concepts of true positives, true negatives, false positives, and false negatives in binary classification. It illustrates how a model's decision boundary affects these metrics and sets the stage for understanding precision, recall, and other alternative metrics to accuracy.