Binary Classifier

Requirements Clarification & Assessment

1. Understanding the Model's Performance Metrics:

   • Accuracy: Measures the proportion of correctly predicted instances out of the total instances. In this case, it's 99%.
   • AUC (Area Under the Curve): Represents the model's ability to distinguish between classes. An AUC of 1 indicates a perfect model, while an AUC of 0.5 suggests random guessing.

2. Contextual Insights:

   • Binary Classification: The task involves two classes, typically labeled as positive and negative.
   • Imbalance Consideration: High accuracy with low AUC often indicates class imbalance, where one class significantly outnumbers the other.

3. Objective:

   • Evaluate the effectiveness of the model based on the given metrics.
   • Determine if the model is genuinely performing well or if the high accuracy is misleading due to underlying issues.