How Ideal Curve looks like?
An ideal ROC curve would be as close as possible to the upper left corner of the plot, indicating high TPR (correctly identifying true positives) with low FPR (incorrectly identifying false positives). The closer the curve is to the diagonal baseline, the worse the classifier’s performance.
The AUC score provides a quantitative measure of the classifier’s performance, with a value of 1 indicating perfect classification and a value of 0.5 indicating no better than random guessing.
How to plot ROC curve in Python
The Receiver Operating Characteristic (ROC) curve is a fundamental tool in the field of machine learning for evaluating the performance of classification models. In this context, we’ll explore the ROC curve and its associated metrics using the breast cancer dataset, a widely used dataset for binary classification tasks.