Which of the following interpretations of the ROC and AUC metrics is MOST ACCURATE for assessing the model’s performance?

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?
A . A ROC curve that is closer to the top-left corner of the plot (AUC ~ 1) shows that the model is overfitting, and its predictions are too optimistic
B . An AUC close to 0 indicates that the model is highly accurate, correctly classifying almost all instances of defaulters and non-defaulters
C . An AUC close to 1.0 indicates that the model has excellent discriminatory power, effectively distinguishing between defaulters and non-defaulters
D . A ROC curve that is close to the diagonal line (AUC ~ 0.5) indicates that the model performs well
across all thresholds

View Answer

Answer: C

Explanation:

Correct option:

An AUC close to 1.0 indicates that the model has excellent discriminatory power, effectively distinguishing between defaulters and non-defaulters

Area Under the (Receiver Operating Characteristic) Curve (AUC) represents an industry-standard accuracy metric for binary classification models. AUC measures the ability of the model to predict a higher score for positive examples as compared to negative examples. Because it is independent of the score cut-off, you can get a sense of the prediction accuracy of your model from the AUC metric without picking a threshold.

The AUC metric returns a decimal value from 0 to 1. AUC values near 1 indicate an ML model that is highly accurate. Values near 0.5 indicate an ML model that is no better than guessing at random. Values near 0 are unusual to see, and typically indicate a problem with the data. Essentially, an AUC near 0 says that the ML model has learned the correct patterns, but is using them to make predictions that are flipped from reality (‘0’s are predicted as ‘1’s and vice versa). The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting.

via –

https://aws.amazon.com/blogs/machine-learning/is-your-model-good-a-deep-dive-into-amazon-sagemaker-canvas-advanced-metrics/

An AUC close to 1.0 signifies that the model has excellent discriminatory power, meaning it can effectively distinguish between the positive class (defaulters) and the negative class (non-defaulters) across all thresholds. This is desirable in a classification task, especially in scenarios with class imbalance.

via – https://docs.aws.amazon.com/machine-learning/latest/dg/binary-model-insights.html

Incorrect options:

A ROC curve that is close to the diagonal line (AUC ~ 0.5) indicates that the model performs well across all thresholds – A ROC curve close to the diagonal line (AUC ~ 0.5) indicates that the model has no discriminatory power and is performing no better than random guessing. This suggests poor model performance, not that the model performs well across all thresholds.

A ROC curve that is closer to the top-left corner of the plot (AUC ~ 1) shows that the model is overfitting, and its predictions are too optimistic – A ROC curve closer to the top-left corner of the plot (AUC closer to 1.0) indicates strong model performance, not overfitting. Overfitting is typically identified by other indicators, such as a large gap between training and validation performance, not by the shape of the ROC curve alone.

An AUC close to 0 indicates that the model is highly accurate, correctly classifying almost all instances of defaulters and non-defaulters – An AUC close to 0 is problematic, as it indicates that the model is consistently making incorrect predictions (i.e., it classifies negatives as positives and vice versa). A high AUC (close to 1) is what signifies strong model performance.

References:

https://docs.aws.amazon.com/machine-learning/latest/dg/binary-model-insights.html https://aws.amazon.com/blogs/machine-learning/creating-high-quality-machine-learning-models-for-financial-services-using-amazon-sagemaker-autopilot/ https://aws.amazon.com/blogs/machine-learning/is-your-model-good-a-deep-dive-into-amazon-sagemaker-canvas-advanced-metrics/