Welcome! Today, we are diving into a fascinating metric in machine learning called AUC-ROC.
Our goal for this lesson is to understand what AUC (Area Under the Curve) and ROC (Receiver Operating Characteristic) are, how to calculate and interpret the AUC-ROC metric, and how to visualize the ROC curve using Python. Ready to explore? Let's get started!
ROC (Receiver Operating Characteristic): This graph shows the performance of a classification model at different threshold settings. It plots the True Positive Rate (TPR) against the False Positive Rate (FPR). In this context, a threshold is a value that determines the cutoff point for classifying a positive versus a negative outcome based on the model's predicted probabilities. For example, if the threshold is set to 0.5, any predicted probability above 0.5 is classified as positive, and anything below is classified as negative. By varying this threshold, we generate different True Positive and False Positive rates, which are then used to plot the ROC curve.
Imagine you have a medical test used to detect a particular disease. True Positive Rate (TPR) measures how effective the test is at correctly identifying patients who have the disease (true positives). False Positive Rate (FPR), on the other hand, measures how often the test incorrectly indicates the disease in healthy patients (false positives).
