What is AUC-ROC in machine learning?

AUC-ROC stands for "area under the receiver operating characteristics curve," which represents the diagnostic capacity of the binary classifier in a graph. The ROC curve is used to visualize the relation between the true positive and false positive rates to examine the trade-off between them. It is used to compute the AUC value that evaluates the classifier's A model that learns to make predictions and assign input data instances to specific classes based on their features. performance and decides if the model is suitable.

ROC curve formula

We plot the rate of correctly classified true positive cases against the rate of incorrectly classified false positive cases to create a ROC curve for the machine learning model.

Here the true positive rate is calculated as follows:

$TPR = TP / (TP+FP)$

And the false positive rate is calculated as follows:

$FPR = FP/(TN + FP)$

Where:

• TP: positive classes that are correctly predicted as positive.

• TN: negative classes that are correctly predicted as negative.

• FP: negative classes that are incorrectly predicted as positive.

ROC curve plot

Once the false positive and true positive rates are obtained by applying the formula, we plot it on a two-dimensional graph. The range of the x-axis and y-axis values is from 0 to 1, and the line drawn within this range is called the ROC curve.

Here are the major ROC curves obtained when evaluating a classifier's performance.

The area under the obtained graph can determine the model's performance. The higher the area, the better the model performance. Above are the two straight-line cases for a perfect ROC with AUC 1.0 and a random ROC with AUC 0.5, and a curve case for a moderate ROC with AUC 0.8.

AUC values interpretation

The AUC values improve the metric interpretability because they provide a range of specific values that correspond to the classifier's performance. Therefore, we can easily interpret how fit the model is and how much training it requires by simply knowing the AUC value and the range it lies in.

Let's take a look at the AUC values and their corresponding interpretations.

Benefits of using AUC

AUC is a very useful metric to identify the effectiveness of the model's performance and detect potential warnings at an early stage.

• It is unaffected by the scaling because it analyses the predictions' ordering rather than measuring the absolute values.

• It evaluates the binary classifier's performance independent of the classification threshold used to determine the positive and negative predictions.

• It can handle the imbalanced data well, better than accuracy, because AUC keeps the entire range in consideration when deriving evaluations.

• It can be applied in different domains for binary classification tasks making it a versatile metric.

Constraints of using AUC

Despite being an easy-to-interpret and threshold-independent performance metric, it does have some limitations where it fails to provide reliable evaluation results.

• In cases where we require an absolutely accurate probability output, AUC can not provide us with that because it assesses the overall performance.

• In a scenario where the optimal solution is to prioritize reducing the false positives rather than focusing on all the classifier categories, AUC statistics are not helpful.

• It is only suitable for binary classifications and can not be applied to multi-class and multi-label classes.

Real-life application

AUC-ROC is used in various major domains to measure the performance of machine learning models.

• Medical diagnostics: Used to evaluate the classifier's performance of accurately diagnosing if the person has a disease or does not have a disease.

• Filtering spam: Used to measure how well the correct and spam messages are filtered in email or messages.

• Fraud detection: Used to assess the classifier's effectiveness to differentiate between legitimate and fraudulent transactions.

• Quality control: Used to assess the performance of classifiers in detecting defective products during the manufacturing processes.

• Object detection: Used to evaluate the model performance in facial recognition and autonomous cars supervision.

Test your understanding

Match the AUC values with the suitable interpretation of the ROC.