This is a dedicated watch page for a single video.
You are a data scientist working on a binary classification model to predict whether customers will default on their loans. The dataset is highly imbalanced, with only 10% of the customers having defaulted in the past. After training the model, you need to evaluate its performance to ensure it effectively distinguishes between defaulters and non-defaulters. Given the class imbalance, accuracy alone is not sufficient to assess the model’s performance. Instead, you decide to use the Receiver Operating Characteristic (ROC) curve and the Area Under the ROC Curve (AUC) to evaluate the model. Which of the following interpretations of the ROC and AUC metrics is MOST ACCURATE for assessing the model’s performance?