Shobha
The best way to predict the future is to invent it
What is the purpose of a ROC curve in model evaluation?
The ROC curve (Receiver Operating Characteristic) visually represents a model’s performance by plotting the rate of correctly classified positive cases (True Positive Rate) against the rate of incorrectly classified negative cases (False Positive Rate) at different thresholds. It helps to select an ideal threshold for decision-making. A high Area Under the Curve (AUC) score indicates the model’s ability to differentiate between classes effectively.For example, In fraud detection, a ROC curve helps determine the threshold that maximizes the detection of fraudulent transactions while minimizing false alarms.