ROC Curve

• appropriate when the data is balanced
• x-axis : False Positive
• y-axis : True Positive
• area under the curve (AUC) can be used as a summary of the model performance
  • assign a higher probability to a randomly chosen real positive occurrence than a negative occurrence on average
• Interpretation
  • Smaller values on the x-axis of the plot indicate lower false positives and higher true negatives.
  • Larger values on the y-axis of the plot indicate higher true positives and lower false negatives.