Models Using ROC Curve Analysis

Requirements Clarification & Assessment

To effectively assess the performance of the three models using the ROC Curve Analysis, we need to clarify the requirements and ensure we understand the task:

1. Objective: Evaluate the performance of three binary classification models using the ROC curve and select the most suitable model based on the Area Under the Curve (AUC).

2. Data: Each model outputs a probability score indicating the likelihood of belonging to class 1.

3. Metrics:

   • ROC Curve: A graphical representation of a model's diagnostic ability.
   • AUC: A single scalar value summarizing the ROC curve's performance.

4. Assumptions:

   • The models are applied to the same dataset, ensuring comparability.
   • The AUC score is a reliable indicator of model performance in this context.

5. Constraints:

   • The models are limited to binary classification.
   • The decision threshold varies to plot the ROC curve.

6. Outcome: Identify the model with the highest AUC score as the preferred classifier.