Written by Coursera Staff • Updated on

Learn how the ROC curve helps you analyze classification algorithms in machine learning.

Receiver operating characteristics (ROC) curves are graphs showing classifiers' performance by plotting the true positive rate and false positive rate. The area under the ROC curve (AUC) measures the performance of machine learning algorithms. ROC curves visually depict the statistical accuracy of classifier selection, but the graph’s original use began in signal detection. Since the 1980s, ROC curves gained popularity in medical diagnostics testing and, more recently, for analyzing the performance of machine learning algorithms.

Read further to discover the uses of the ROC curve, how AUC works, uses for the ROC curve in machine learning, and the pros and cons of ROC curves.

To help understand when to use an ROC curve, let’s examine some other common classification metrics in machine learning:

**Confusion matrix:**A generated two-column matrix of all the true positives, false positives, true negatives, and false negatives that a machine learning algorithm produced

**Recall:**The ratio of positive values to predicted positive values, also called sensitivity or the true positive rate in machine learning

**Specificity:**The ratio of negative values predicted to be negative values, also called specificity or the true negative rate in machine learning

**Precision:**The ratio of true positives in predicted to all positives predicted, also called the positive predictive value

**Accuracy:**The ratio of values the machine learning algorithm predicted correctly calculated by combining the true positive and true negative divided by all values

**F1-score:**The harmonic mean of precision and sensitivity, also called an F score

**Precision-recall curve:**A graph of precision and recall that uses the area under the precision-recall curve (AUCPRC) to find the total average precision score

An ROC curve works by plotting the true positive rate (TPR) on the y-axis and the false positive rate (FPR) on the x-axis of a graph. How does this connect to classification metrics in machine learning? Once a classification model has analyzed training data, a confusion matrix displays the results of the predicted data against the labeled data, and you can use this data—the TPR and the FPR—to produce the ROC curve, which can help you determine the efficacy of your machine learning model. Now, let’s examine what makes up a TPR and FPR in machine learning:

**True positive rate:**A ratio of true positive predictions divided by the true positives plus false negative predictions (TPR = TP / TP + FN)

**False positive rate:**A ratio of total false positive predictions divided by the false positive plus true negative predictions (FPR = FP / FP + TN)

The true positive and false positive rates at each point on the curve depict the rate at each decision classification threshold. To create the ROC curve, the scale goes from zero to one, with an ideal rate being one for positives and zero for negatives. The ROC curve has no bias towards classifiers and remains independent of the conditions it works under, making it useful for predictions with both balanced and imbalanced problems.

A score is given to them to compare the ROC curve of multiple classifiers based on a calculation of the area under the ROC curve, also known as AUC or ROCAUC. This score ranges from 0.0 to 1.0, with 1.0 being a perfect classifier. To calculate AUC, you use trapezoidal integration, which means first using the FPR and TPR values to divide the area under the ROC curve into trapezoids. Then, you add together the areas of those trapezoids to determine the AUC. The AUC is the average probability of how a model will classify positive responses and negative responses.

AUC measures the ranking of predictions, not the accuracy of independent values, making it useful for telling you how well a model makes predictions regardless of the classification. This, however, makes AUC not a useful test if you need to examine the accuracy of individual classifications.

In machine learning, ROC curves measure the performance of various machine learning algorithm classifications. In conjunction with the use of AUC, ROC curves show how well an algorithm classifies objects through the invariance of AUC when it comes to the class being analyzed. A ROC curve focuses on finding the errors and benefits classifiers use to organize classes, making ROC graphs a useful analysis when comparing two classes in something like a diagnostic test that tests whether a condition is present or not present in an individual class.

Along with being used to evaluate machine learning algorithms, health care professionals frequently use ROC curves and AUC when examining medical diagnostics tests. For testing, it helps determine the accuracy of the tests by comparing multiple diagnostics to each other to make an accurate diagnosis. The ROC curve in the medical field also helps predict health outcomes for patients prone to certain health risks. It also has uses in epidemiology, radiology, and bioinformatics. From the highs and lows of the stock market to the survival of fruit trees or the winning and losing in sports, ROC curves are a fundamental piece of examining predictions from any binary model.

While ROC methodology has useful properties for analyzing classification algorithms, this tool does contain some drawbacks. Let’s take a look at some of the pros and cons of ROC curves in machine learning classification.

The ROCAUC has a better aptitude for the analysis of variance tests. It works independently of any decision thresholds made and shows how well the algorithm separates a particular class's positives and negatives. Additionally, it has invariance to class probabilities and shows how a classification algorithm works by ruling out random or singular classes with low AUC scores. Let’s review a few brief pros:

AUC score uses one number to define classification, which means you can quickly compare various algorithms to each other.

While experimenting with different models, invariance to class probability allows you to compare the quality of the models.

The ROCAUC provides a visual representation of separate positives and negatives of a class.

Finally, because it’s a graph, it visually shows how well an algorithm separates classes.

While ROCAUC is a powerful tool, careful implementation is required to avoid bias and perform a proper analysis. While the AUC number is helpful if you have a background in data science or statistics, the singular AUC number may cause issues in communicating with business leaders who might need more context on the importance of the number when making business decisions. Let’s review the cons of ROC curves and the AUC.

The significance of the AUC score can be difficult to communicate to executives who don’t understand the methodology.

Another disadvantage of ROCAUC is that it considers inconsequential areas of the ROC space when determining the overall quality of the model’s performance.

Finally, ROCAUC does not work well when dealing with a large imbalance. For example, if the positive class is small and the negative class is large, the graph will skew the positive class and make it appear like it has a much higher quality than it actually does. A precision-recall curve might be more effective when analyzing this type of imbalance.

Finally, if you’re interested in more than understanding ROC curves and how they measure machine learning models, pursuing a career as an artificial intelligence engineer is a well-paying job possibility. According to Glassdoor, your average annual salary in this position could be $115,655 [1]. Additionally, the US Bureau of Labor Statistics states that career opportunities within this field are expected to grow 23 percent from 2022 to 2032, which is much faster than average [2].

Expand your understanding of classification metrics and use the ROC curve to analyze machine learning models by exploring data analysis with Python specialization from the University of Colorado Boulder. Take your data science career to the next step. This Specialization contains a course on classification evaluation and is found on Coursera.

1.

Glassdoor. “How much does an AI Engineer make?, https://www.glassdoor.com/Salaries/ai-engineer-salary-SRCH_KO0,11.htm.” Accessed March 19, 2024.

2.

US Bureau of Labor Statistics. “Computer and Information Research Scientists, https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm.” Accessed March 19, 2024.

Updated on

Written by:### Coursera Staff

C

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.