 # Introduction to the ROC (Receiver Operating Characteristics) plot

The Receiver Operating Characteristics (ROC) plot is a popular measure for evaluating classifier performance. ROC has been used in a wide range of fields, and the characteristics of the plot is also well studied. We cover the basic concept and several important aspects of the ROC plot through this page.

For those who are not familiar with the basic measures derived from the confusion matrix or the basic concept of model-wide evaluation, we recommend reading the following two pages.

If you want to dive into making nice ROC plots right away, have a look at the tools page:

## ROC shows trade-offs between sensitivity and specificity

The ROC plot is a model-wide evaluation measure that is based on two basic evaluation measures – specificity and sensitivity. Specificity is a performance measure of the whole negative part of a dataset, whereas sensitivity is a performance measure of the whole positive part. A dataset has two labels (P and N), and a classifier separates the dataset into four outcomes – TP, TN, FP, FN. The ROC plot is based on two basic measures – specificity and sensitivity that are calculated from the four outcomes.

The ROC plot uses 1 – specificity on the x-axis and sensitivity on the y-axis. False positive rate (FPR) is identical with 1 – specificity, and true positive rate (TPR) is identical with sensitivity.

### Making a ROC curve by connecting ROC points

A ROC point is a point with a pair of x and y values in the ROC space where x is 1 – specificity and y is sensitivity. A ROC curve is created by connecting all ROC points of a classifier in the ROC space. Two adjacent ROC points can be connected by a straight line, and the curve starts at (0.0, 0.0) and ends at (1.0, 1.0).

#### An example of making a ROC curve

We show a simple example to make a ROC curve by connecting several ROC points. Let us assume that we have calculated sensitivity and specificity values from multiple confusion matrices for four different threshold values.

Threshold Sensitivity Specificity 1 – specificity
1 0.0 1.0 0.0
2 0.5 0.75 0.25
3 0.75 0.5 0.5
4 1.0 0.0 1.0

We first added four points that matches with the pairs of sensitivity and specificity values and then connected the points to create a ROC curve.

## Interpretation of ROC curves

Easy interpretation of a ROC curve is one of the advantages of using the ROC plot. We show how to interpret ROC curves with several examples.

### A ROC curve of a random classifier

A classifier with the random performance level always shows a straight line from the origin (0.0, 0.0) to the top right corner (1.0, 1.0). Two areas separated by this ROC curve indicates a simple estimation of the performance level. ROC curves in the area with the top left corner (0.0, 1.0) indicate good performance levels, whereas ROC curves in the other area with the bottom right corner (1.0, 0.0) indicate poor performance levels. A ROC curve represents a classifier with the random performance level. The curve separates the space into two areas for good and poor performance levels.

### A ROC curve of a perfect classifier

A classifier with the perfect performance level shows a combination of two straight lines – from the origin (0.0, 0.0) to the top left corner (0.0, 1.0) and further to the top right corner (1.0, 1.0). A ROC curve represents a classifier with the perfect performance level.

It is important to notice that classifiers with meaningful performance levels usually lie in the area between the random ROC curve (baseline) and the perfect ROC curve.

### ROC curves for multiple models

Comparison of multiple classifiers is usually straight-forward especially when no curves cross each other. Curves close to the perfect ROC curve have a better performance level than the ones closes to the baseline. Two ROC curves represent the performance levels of two classifiers A and B. Classifier A clearly outperforms classifier B in this example.

## AUC (Area under the ROC curve) score

Another advantage of using the ROC plot is a single measure called the AUC (area under the ROC curve) score. As the name indicates, it is an area under the curve calculated in the ROC space. One of the easy ways to calculate the AUC score is using the trapezoidal rule, which is adding up all trapezoids under the curve. The AUC score can be calculated by the trapezoidal rule, which is adding up all trapezoids under the curve. The areas of the three trapezoids 1, 2, 3 are 0.0625, 0.15625, and 0.4375. The AUC score is then 0.65625.

Although the theoretical range of AUC score is between 0 and 1, the actual scores of meaningful classifiers are greater than 0.5, which is the AUC score of a random classifier. It shows four AUC scores. The score is 1.0 for the classifier with the perfect performance level (P) and 0.5 for the classifier with the random performance level (R). ROC curves clearly shows classifier A outperforms classifier B, which is also supported by their AUC scores (0.88 and 0.72).

## 3 aspects that can be problematic with the ROC analysis

All performance measures have their advantages and disadvantages. Even thought ROC is a powerful performance measure, the performance evaluation is likely inaccurate if the plot is interpreted in a wrong way.

### Use dissimilar datasets in one ROC plot

This is a common mistake when ROC is used for comparing multiple classifiers. One ROC curve with several ROC points are drawn in one plot. The comparison between them is valid only when the classifiers are evaluated on either a single dataset or multiple datasets that are almost identical among each other in terms of their data size and positive:negative ratio. ROC curves and ROC points cannot be compared if they have been generated from different datasets. All classifiers, A, B, C, and D, should be tested on the same dataset for a valid comparison.

### Rely only on AUC scores

AUC scores are convenient to compare multiple classifiers. Nonetheless, it is also important to check the actual curves especially when evaluating the final model. Even when two ROC curves have the same AUC values, the actual curves can be quite different. Two classifiers A and B have the same AUC scores, but their ROC curves are different.

### ROC analysis in conjunction with imbalanced datasets

ROC becomes less powerful when used with imbalanced datasets. One effective approach to avoid the potential issues with imbalanced datasets is using the early retrieval area, which is a region with high specificity values in the ROC space. Checking this area is useful to analyse the performance with fewer false positives (or small false positive rate). The yellow region of the ROC plot indicates the early retrieval area. It is useful to check this area especially when the dataset is imbalanced.

Moreover, the AUC score can be calculated for any partial area of the ROC curve. The most common approach is calculating ROC50, which adds up true positives until the number of false positives reaches 50. The number of false positives can be any value instead of 50 depending on the data size and some other criteria.

Analysing the early retrieval of ROC is not sufficient in some cases, however. For more details about the potential issues with imbalanced datasets, we recommend reading the following pages.

## 5 thoughts on “Introduction to the ROC (Receiver Operating Characteristics) plot”

1. S P says:

your plots here are beautiful. Can you post the R code used to generate these plots?

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1. takaya says:

Hi,

I used Precrec, which is an R package we’ve developed, to produce vector image files, such as pdf and eps. I then edited these files with a vector graphics application.

Vector graphics editors

• Inkscape (site)

Code snippet
``` # Load libraries library(precrec) library(ggplot2)```

``` # Set scores and labels scores <- c(1.9, 1.3, 2.8, 0.5, 0.2, 0.1, 2.5, 2.2) labels <- c(1, 0, 1, 1, 0, 0, 1, 0) # Calculate ROC and precision-recall curves <- evalmod(scores = scores, labels = labels) # Plot with ROC and precision-recall autoplot(curves) ```

```# Plot with ROC only autoplot(curves, curvetype = c("ROC")) ```

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2. B J says:

I am new to ROC curves and have a question about how they are used for model building. In your example you have two classifiers: A and B. You show that A outclassifies B with a higher AUC and better curve, but I am wondering if you can combine A and B and assess this joint ability of the two tests to see if this will enhance the predictive ability. Or is it not appropriate to combine?

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1. takaya says:

You can use ROC curves for comparing different hypotheses (candidates of your final model) and different models (developed by different machine learning methods, for instance), with their AUC scores indicating their prediction performance.

>if you can combine A and B and assess this joint ability of the two tests to see if this will enhance the predictive ability

Combining curves doesn’t make sense in the ROC plot. You may combine models and hypotheses during training using a meta-algorithm, such as ensemble, boosting, and multiple classifier systems, but not at validation or evaluation phases.

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