How do I evaluate my model using ROC AUC?
An example of an AI chatbot conversation about evaluating models using ROC AUC metrics.
How do I evaluate my model using ROC AUC?
ROC AUC is a performance measurement for classification problems. It tells you how well your model distinguishes between classes.
What does ROC stand for?
ROC stands for Receiver Operating Characteristic. It's a graphical plot that illustrates the diagnostic ability of a binary classifier.
How do I calculate the AUC?
AUC, or Area Under the Curve, can be calculated using the trapezoidal rule on the ROC curve. Many libraries like scikit-learn provide built-in functions for this.
What is a good AUC score?
AUC scores range from 0 to 1. A score of 0.5 indicates no discrimination, while a score of 1 indicates perfect discrimination.
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