ROC Curve Analysis (Model Performance Evidence)

The ROC (Receiver Operating Characteristic) curve illustrates the performance of the Decision Tree classifier in distinguishing between the two classes: Poor and Fair credit scores.

Evidence of Model Performance

  • AUC Score: 0.75
  • Random Classifier Baseline: A diagonal reference line representing no discrimination ability (AUC = 0.50)
ROC Curve for Decision Tree Model

As shown in the ROC curve, the model’s performance curve lies consistently above the diagonal baseline, indicating that it performs significantly better than random guessing.

Interpretation

An AUC value of 0.75 indicates that the model has a 75% probability of ranking a randomly chosen positive instance higher than a randomly chosen negative instance. In other words, the classifier demonstrates acceptable to good discriminatory performance for a baseline Decision Tree model.

Conclusion: The ROC curve confirms that the model outperforms a random classifier and demonstrates reliable ability to distinguish between the two credit score classes.

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