## ROC Curve and Performance Metrics

This topic describes the performance metrics for classification, including the receiver operating characteristic (ROC) curve and the area under a ROC curve (AUC), and introduces the Statistics and Machine Learning Toolbox™ object `rocmetrics`, which you can use to compute performance metrics for binary and multiclass classification problems.

### Introduction to ROC Curve

After training a classification model, such as `ClassificationNaiveBayes` or `ClassificationEnsemble`, you can examine the performance of the algorithm on a specific test data set. A common approach is to compute a gross measure of performance, such as quadratic loss or accuracy, averaged over the entire test data set. You can inspect the classifier performance more closely by plotting a ROC curve and computing performance metrics. For example, you can find the threshold that maximizes the classification accuracy, or assess how the classifier performs in the regions of high sensitivity and high specificity.

#### Receiver Operating Characteristic (ROC) Curve

A ROC curve shows the true positive rate (TPR, or sensitivity) versus the false positive rate (FPR, or 1-specificity) for different thresholds of classification scores.

Each point on a ROC curve corresponds to a pair of TPR and FPR values for a specific threshold value. You can find different pairs of TPR and FPR values by varying the threshold value, and then create a ROC curve using the pairs.

For a multiclass classification problem, you can use the one-versus-all coding design and find a ROC curve for each class. The one-versus-all coding design treats a multiclass classification problem as a set of binary classification problems, and assumes one class as positive and the rest as negative in each binary problem.

A binary classifier typically classifies an observation into a class that yields a larger score, which corresponds to a positive adjusted score for a one-versus-all binary classification problem. That is, a classifier typically uses 0 as a threshold and determines whether an observation is positive or negative. For example, if an adjusted score for an observation is 0.2, then the classifier with a threshold value of 0 assigns the observation to the positive class. You can find a pair of TPR and FPR values by applying the threshold value to all observations, and use the pair as a single point on a ROC curve. Now, assume you use a new threshold value of 0.25. Then, the classifier with a threshold value of 0.25 assigns the observation with an adjusted score of 0.2 to the negative class. By applying the new threshold to all observations, you can find a new pair of TPR and FPR values and have a new point on the a ROC curve. By repeating this process for various threshold values, you find pairs of TPR and FPR values and create a ROC curve using the pairs.

#### Area Under ROC Curve (AUC)

The area under a ROC curve (AUC) corresponds to the integral of a ROC curve (TPR values) with respect to FPR from `FPR` = `0` to `FPR` = `1`.

The AUC provides an aggregate performance measure across all possible thresholds. The AUC values are in the range `0` to `1`, and larger AUC values indicate better classifier performance.

• A perfect classifier always correctly assigns positive class observations to the positive class and has a true positive rate of `1` for any threshold values. Therefore, the line passing through `[0,0]`, `[0,1]`, and `[1,1]` represents the perfect classifier, and the AUC value is `1`.

• A random classifier returns random score values and has the same values for the false positive rate and true positive rate for any threshold values. Therefore, the ROC curve for the random classifier lies on the diagonal line, and the AUC value is `0.5`.

### Performance Curve with MATLAB

You can compute a ROC curve and other performance curves by creating a `rocmetrics` object. The `rocmetrics` object supports both binary and multiclass classification problems and provides the following object functions:

• `plot` — Plot ROC or other classifier performance curves. `plot` returns a `ROCCurve` graphics object for each curve. You can modify the properties of the objects to control the appearance of each curve. For details, see ROCCurve Properties.

• `average` — Compute performance metrics for an average ROC curve for multiclass problems.

• `addMetrics` — Compute additional classification performance metrics.

You can also compute the confidence intervals of performance curves by providing cross-validated inputs or by bootstrapping the input data.

After training a classifier, use a performance curve to evaluate the classifier performance on test data. Various measures such as mean squared error, classification error, or exponential loss can summarize the predictive power of a classifier in a single number. However, a performance curve offers more information because it lets you explore the classifier performance across a range of thresholds on the classification scores.

#### Plot ROC Curve for Binary Classifier

Compute the performance metrics (FPR and TPR) for a binary classification problem by creating a `rocmetrics` object, and plot a ROC curve by using `plot` function.

Load the `ionosphere` data set. This data set has 34 predictors (`X`) and 351 binary responses (`Y`) for radar returns, either bad (`'b'`) or good (`'g'`).

`load ionosphere`

Partition the data into training and test sets. Use approximately 80% of the observations to train a support vector machine (SVM) model, and 20% of the observations to test the performance of the trained model on new data. Partition the data using `cvpartition`.

```rng("default") % For reproducibility of the partition c = cvpartition(Y,Holdout=0.20); trainingIndices = training(c); % Indices for the training set testIndices = test(c); % Indices for the test set XTrain = X(trainingIndices,:); YTrain = Y(trainingIndices); XTest = X(testIndices,:); YTest = Y(testIndices);```

Train an SVM classification model.

`Mdl = fitcsvm(XTrain,YTrain);`

Compute the classification scores for the test set.

```[~,Scores] = predict(Mdl,XTest); size(Scores)```
```ans = 1×2 70 2 ```

The output `Scores` is a matrix of size `70`-by-`2`. The column order of `Scores` follows the class order in `Mdl`. Display the class order stored in `Mdl.ClassNames`.

`Mdl.ClassNames`
```ans = 2x1 cell {'b'} {'g'} ```

Create a `rocmetrics` object by using the true labels in `YTest` and the classification scores in `Scores`. Specify the column order of `Scores` using `Mdl.ClassNames`.

`rocObj = rocmetrics(YTest,Scores,Mdl.ClassNames);`

`rocObj` is a `rocmetrics` object that stores the AUC values and performance metrics for each class in the `AUC` and `Metrics` properties. Display the `AUC` property.

`rocObj.AUC`
```ans = 1×2 0.8587 0.8587 ```

For a binary classification problem, the AUC values are equal to each other.

The table in `Metrics` contains the performance metric values for both classes, vertically concatenated according to the class order. Find the rows for the first class in the table, and display the first eight rows.

```idx = strcmp(rocObj.Metrics.ClassName,Mdl.ClassNames(1)); head(rocObj.Metrics(idx,:))```
``` ClassName Threshold FalsePositiveRate TruePositiveRate _________ _________ _________________ ________________ {'b'} 15.545 0 0 {'b'} 15.545 0 0.04 {'b'} 15.105 0 0.08 {'b'} 11.424 0 0.16 {'b'} 10.077 0 0.2 {'b'} 9.9716 0 0.24 {'b'} 9.9417 0 0.28 {'b'} 9.0338 0 0.32 ```

Plot the ROC curve for each class by using the `plot` function.

`plot(rocObj)`

For each class, the `plot` function plots a ROC curve and displays a filled circle marker at the model operating point. The legend displays the class name and AUC value for each curve.

Note that you do not need to examine ROC curves for both classes in a binary classification problem. The two ROC curves are symmetric, and the AUC values are identical. A TPR of one class is a true negative rate (TNR) of the other class, and TNR is 1-FPR. Therefore, a plot of TPR versus FPR for one class is the same as a plot of 1-FPR versus 1-TPR for the other class.

Plot the ROC curve for the first class only by specifying the `ClassNames` name-value argument.

`plot(rocObj,ClassNames=Mdl.ClassNames(1))`

#### Plot ROC Curves for Multiclass Classifier

Compute the performance metrics (FPR and TPR) for a multiclass classification problem by creating a `rocmetrics` object, and plot a ROC curve for each class by using the `plot` function. Specify the `AverageROCType` name-value argument of `plot` to create the average ROC curve for the multiclass problem.

Load the `fisheriris` data set. The matrix `meas` contains flower measurements for 150 different flowers. The vector `species` lists the species for each flower. `species` contains three distinct flower names.

`load fisheriris`

Train a classification tree that classifies observations into one of the three labels. Cross-validate the model using 10-fold cross-validation.

```rng("default") % For reproducibility Mdl = fitctree(meas,species,Crossval="on");```

Compute the classification scores for validation-fold observations.

```[~,Scores] = kfoldPredict(Mdl); size(Scores)```
```ans = 1×2 150 3 ```

The output `Scores` is a matrix of size `150`-by-`3`. The column order of `Scores` follows the class order in `Mdl`. Display the class order stored in `Mdl.ClassNames`.

`Mdl.ClassNames`
```ans = 3x1 cell {'setosa' } {'versicolor'} {'virginica' } ```

Create a `rocmetrics` object by using the true labels in `species` and the classification scores in `Scores`. Specify the column order of `Scores` using `Mdl.ClassNames`.

`rocObj = rocmetrics(species,Scores,Mdl.ClassNames);`

`rocObj` is a `rocmetrics` object that stores the AUC values and performance metrics for each class in the `AUC` and `Metrics` properties. Display the `AUC` property.

`rocObj.AUC`
```ans = 1×3 1.0000 0.9636 0.9636 ```

The table in `Metrics` contains the performance metric values for all three classes, vertically concatenated according to the class order. Find and display the rows for the second class in the table.

```idx = strcmp(rocObj.Metrics.ClassName,Mdl.ClassNames(2)); rocObj.Metrics(idx,:)```
```ans=13×4 table ClassName Threshold FalsePositiveRate TruePositiveRate ______________ _________ _________________ ________________ {'versicolor'} 1 0 0 {'versicolor'} 1 0.01 0.7 {'versicolor'} 0.95455 0.02 0.8 {'versicolor'} 0.91304 0.03 0.9 {'versicolor'} -0.2 0.04 0.9 {'versicolor'} -0.33333 0.06 0.9 {'versicolor'} -0.6 0.08 0.9 {'versicolor'} -0.86957 0.12 0.92 {'versicolor'} -0.91111 0.16 0.96 {'versicolor'} -0.95122 0.31 0.96 {'versicolor'} -0.95238 0.38 0.98 {'versicolor'} -0.95349 0.44 0.98 {'versicolor'} -1 1 1 ```

Plot the ROC curve for each class. Specify `AverageROCType="micro"` to compute the performance metrics for the average ROC curve using the micro-averaging method.

`plot(rocObj,AverageROCType="micro")`

The filled circle markers indicate the model operating points. The legend displays the class name and AUC value for each curve.

### ROC Curve for Multiclass Classification

For a multiclass classifier, the `rocmetrics` function computes the performance metrics of a one-versus-all ROC curve for each class, and the `average` function computes the metrics for an average of the ROC curves. You can use the `plot` function to plot a ROC curve for each class and the average ROC curve.

#### One-Versus-All (OVA) Coding Design

The one-versus-all (OVA) coding design reduces a multiclass classification problem to a set of binary classification problems. In this coding design, each binary classification treats one class as positive and the rest of the classes as negative. `rocmetrics` uses the OVA coding design for multiclass classification and evaluates the performance on each class by using the binary classification that the class is positive.

For example, the OVA coding design for three classes formulates three binary classifications:

Each row corresponds to a class, and each column corresponds to a binary classification problem. The first binary classification assumes that class 1 is a positive class and the rest of the classes are negative. `rocmetrics` evaluates the performance on the first class by using the first binary classification problem.

`rocmetrics` applies the OVA coding design to a binary classification problem as well if you specify classification scores as a two-column matrix. `rocmetrics` formulates two one-versus-all binary classification problems each of which treats one class as a positive class and the other class as a negative class, and `rocmetrics` finds two ROC curves. You can use one of them to evaluate the binary classification problem.

#### Average of Performance Metrics

You can compute metrics for an average ROC curve by using the `average` function. Alternatively, you can use the `plot` function to compute the metrics and plot the average ROC curve. For examples, see Find Average ROC Curve (example for `average`) and Plot Average ROC Curve for Multiclass Classifier (example for `plot`).

`average` and `plot` support three algorithms for computing the average false positive rate (FPR) and average true positive rate (TPR) to find the average ROC curve:

• Micro-averaging — The software combines all one-versus-all binary classification problems into one binary classification problem and computes the average performance metrics as follows:

1. Convert the values in the `Labels` property of a `rocmetrics` object to logical values where logical `1` (`true`) indicates a positive class for each binary problem.

2. Stack the converted vectors of labels, one vector from each binary problem, into a single vector.

3. Convert the matrix that contains the adjusted values of the classification scores (the `Scores` property) into a vector by stacking the columns of the matrix.

4. Compute the components of the confusion matrix for the combined binary problem for each threshold (each distinct value of adjusted scores). A confusion matrix contains the number of instances for true positive (TP), false negative (FN), false positive (FP), and true negative (TN).

5. Compute the average FPR and TPR based on the components of the confusion matrix.

• Macro-averaging — The software computes the average values for FPR and TPR by averaging the values of all one-versus-all binary classification problems.

The software uses three metrics—threshold, FPR, and TPR—to compute the average values as follows:

1. Determine a fixed metric. If you specify `FixedMetric` of `rocmetrics` as `"FalsePositiveRate"` or `"TruePositiveRate"`, then the function holds the specified metric fixed. Otherwise, the function holds the threshold values fixed.

2. Find all distinct values in the `Metrics` property for the fixed metric.

3. Find the corresponding values for the other two metrics for each binary problem.

4. Average the FPR and TPR values of all binary problems.

• Weighted macro-averaging — The software computes the weighted average values for FPR and TPR using the macro-averaging algorithm and using the prior class probabilities (the `Prior` property) as weights.

### Performance Metrics

The `rocmetrics` object supports these built-in performance metrics:

• Number of true positives (TP)

• Number of false negatives (FN)

• Number of false positives (FP)

• Number of true negatives (TN)

• Sum of TP and FP

• Rate of positive predictions (RPP)

• Rate of negative predictions (RNP)

• Accuracy

• True positive rate (TPR), recall, or sensitivity

• False negative rate (FNR), or miss rate

• False positive rate (FPR), fallout, or 1-specificity

• True negative rate (TNR), or specificity

• Positive predictive value (PPV), or precision

• Negative predictive value (NPR)

• Expected cost

`rocmetrics` also supports a custom metric specified as a function handle. For details, see the `AdditionalMetrics` name-value argument of the `rocmetrics` function.

`rocmetrics` computes performance metric values for various thresholds for each one-versus-all binary classification problem using a confusion matrix, scale vector, and misclassification cost matrix. Each performance metric is a function of a confusion matrix and scale vector. The expected cost is also a function of the misclassification cost matrix, as is a custom metric.

• Confusion matrix — A confusion matrix contains the number of instances for true positive (TP), false negative (FN), false positive (FP), and true negative (TN). `rocmetrics` computes confusion matrices for various threshold values for each binary problem.

• Scale vector — A scale vector is defined by the prior class probabilities and the number of classes in true labels. `rocmetrics` finds the probabilities and number of classes for each binary problem from the prior class probabilities specified by the `Prior` name-value argument and the true labels specified by the `Labels` input argument.

• Misclassification cost matrix — `rocmetrics` converts the misclassification cost matrix specified by the `Cost` name-value argument to the values for each binary problem.

By default, `rocmetrics` uses all distinct adjusted score values as threshold values for each binary problem. For more details on threshold values, see Thresholds, Fixed Metric, and Fixed Metric Values.

#### Confusion Matrix

A confusion matrix is defined as

`$\left[\begin{array}{cc}TP& FN\\ FP& TN\end{array}\right],$`

where

• `P` stands for "positive".

• `N` stands for "negative".

• `T` stands for "true".

• `F` stands for "false".

For example, the first row of the confusion matrix defines how the classifier identifies instances of the positive class: TP is the count of correctly identified positive instances, and FN is the count of positive instances misidentified as negative.

`rocmetrics` computes confusion matrices for various threshold values for each one-versus-all binary classification. The one-versus-all binary classification model classifies an observation into a positive class if the score for the observation is greater than or equal to the threshold value.

#### Prior Class Probabilities

By default, `rocmetrics` uses empirical probabilities, which are class frequencies in the true labels.

`rocmetrics` normalizes the `1`-by-K prior probability vector π to a `1`-by-`2` vector for each one-versus-all binary classification, where K is the number of classes.

The prior probabilities for the kth binary classification in which the positive class is the kth class is $\left[{\pi }_{k},1-{\pi }_{k}\right]$, where πk is the prior probability for class k in the multiclass problem.

#### Scale Vector

`rocmetrics` defines a scale vector sk of size `2`-by-`1` for each one-versus-all binary classification problem:

`${\text{s}}_{k}=\frac{1}{{\pi }_{k}N+\left(1-{\pi }_{k}\right)P}\left[\begin{array}{c}{\pi }_{k}N\\ \left(1-{\pi }_{k}\right)P\end{array}\right],$`

where P and N represent the total instances of positive class and negative class, respectively. That is, P is the sum of TP and FN, and N is the sum of FP and TN. sk(1) (first element of sk) and sk(2) (second element of sk) are the scales for the positive class (kth class) and negative class (the rest), respectively.

`rocmetrics` applies the scale values as multiplicative factors to the counts from the corresponding class. That is, the function multiplies counts from the positive class by sk(1) and counts from the negative class by sk(2). For example, to compute the positive predictive value (```PPV = TP/(TP+FP)```) for the kth binary problem, `rocmetrics` scales `PPV` as follows:

`$PPV=\frac{{s}_{k}\left(1\right)\cdot TP}{{s}_{k}\left(1\right)\cdot TP+{s}_{k}\left(2\right)\cdot FP}.$`

#### Misclassification Cost Matrix

By default, `rocmetrics` uses a K-by-K cost matrix C, where C(i,j) = `1` if i ~= j, and C(i,j) = `0` if i = j. C(i,j) is the cost of classifying a point into class j if its true class is i (that is, the rows correspond to the true class and the columns correspond to the predicted class).

`rocmetrics` normalizes the K-by-K cost matrix C to a `2`-by-`2` matrix for each one-versus-all binary classification:

`${C}_{k}=\left[\begin{array}{cc}0& {\text{cost}}_{k}\left(N|P\right)\\ {\text{cost}}_{k}\left(P|N\right)& 0\end{array}\right].$`

Ck is the cost matrix for the kth binary classification in which the positive class is the kth class, where `costk(N|P)` is the cost of misclassifying a positive class as a negative class, and `costk(P|N)` is the cost of misclassifying a negative class as a positive class.

For class k, let πk+ and πk- be K-by-`1` vectors with the following values:

πki+ and πki- are the ith elements of πk+ and πk-, respectively.

The cost of classifying a positive-class (class k) observation into the negative class (the rest) is

`${\text{cost}}_{k}\left(N|P\right)={\left({\pi }_{k}^{+}\right)}^{\prime }C{\pi }_{k}^{-}.$`

Similarly, the cost of classifying a negative-class observation into the positive class is

`${\text{cost}}_{k}\left(P|N\right)={\left({\pi }_{k}^{-}\right)}^{\prime }C{\pi }_{k}^{+}.$`

### Classification Scores and Thresholds

The `rocmetrics` function determines threshold values from the input classification scores or the `FixedMetricValues` name-value argument.

#### Classification Score Input for `rocmetrics`

`rocmetrics` accepts classification scores (`Scores`) in a matrix of size n-by-K or a vector of length n, where n is the number of observations and K is the number classes. For cross-validated data, `Scores` can be a cell array of vectors or a cell array of matrices.

• Matrix of size n-by-K — Specify `Scores` using the second output argument of the `predict` function of a classification model object (such as `predict` of `ClassificationTree`). Each row of the output contains classification scores for an observation for all classes, and the column order of the output matches the class order in the `ClassNames` property of the classification model object. You can specify `Scores` as a matrix for both binary classification and multiclass classification problems.

If you use a matrix format, `rocmetrics` adjusts the classification scores for each class relative to the scores for the rest of the classes. Specifically, the adjusted score for a class given an observation is the difference between the score for the class and the maximum value of the scores for the rest of the classes. For more details, see Adjusted Scores for Multiclass Classification Problem.

• Vector of length n — Specify `Scores` using a vector when you have classification scores for one class only. A vector input is also suitable when you want to use a different type of adjusted scores for a multiclass problem. As an example, consider a problem with three classes, `A`, `B`, and `C`. If you want to compute a performance curve for separating classes `A` and `B`, with `C` ignored, you need to address the ambiguity in selecting `A` over `B`. You can use the score ratio `s(A)/s(B)` or score difference `s(A)–s(B)` and pass the vector to `rocmetrics`; this approach can depend on the nature of the scores and their normalization.

You can use `rocmetrics` with any classifier or any function that returns a numeric score for an instance of input data.

• A high score returned by a classifier for a given instance and class signifies that the instance is likely from the respective class.

• A low score signifies that the instance is not likely from the respective class.

For some classifiers, you can interpret the score as the posterior probability of observing an instance of a class given an observation. An example of such a score is the fraction of observations for a certain class in a leaf of a decision tree. In this case, scores fall into the range from 0 to 1, and scores from all classes add up to 1. Other functions can return scores ranging between minus and plus infinity, without any obvious mapping from the score to the posterior class probability.

`rocmetrics` does not impose any requirements on the input score range. Because of this lack of normalization, you can use `rocmetrics` to process scores returned by any classification, regression, or fit functions. `rocmetrics` does not make any assumptions about the nature of input scores.

`rocmetrics` is intended for use with classifiers that return scores, not those that return only predicted classes. Consider a classifier that returns only classification labels, 0 or 1, for data with two classes. In this case, the performance curve reduces to a single point because the software can split classified instances into positive and negative categories in one way only.

#### Adjusted Scores for Multiclass Classification Problem

For each class, `rocmetrics` adjusts the classification scores (input argument `Scores` of `rocmetrics`) relative to the scores for the rest of the classes if you specify `Scores` as a matrix. Specifically, the adjusted score for a class given an observation is the difference between the score for the class and the maximum value of the scores for the rest of the classes.

For example, if you have [s1,s2,s3] in a row of `Scores` for a classification problem with three classes, the adjusted score values are [s1-`max`(s2,s3),s2-`max`(s1,s3),s3-`max`(s1,s2)].

`rocmetrics` computes the performance metrics using the adjusted score values for each class.

For a binary classification problem, you can specify `Scores` as a two-column matrix or a column vector. Using a two-column matrix is a simpler option because the `predict` function of a classification object returns classification scores as a matrix, which you can pass to `rocmetrics`. If you pass scores in a two-column matrix, `rocmetrics` adjusts scores in the same way that it adjusts scores for multiclass classification, and it computes performance metrics for both classes. You can use the metric values for one of the two classes to evaluate the binary classification problem. The metric values for a class returned by `rocmetrics` when you pass a two-column matrix are equivalent to the metric values returned by `rocmetrics` when you specify classification scores for the class as a column vector.

#### Model Operating Point

The model operating point represents the FPR and TPR corresponding to the typical threshold value.

The typical threshold value depends on the input format of the `Scores` argument (classification scores) specified when you create a `rocmetrics` object:

• If you specify `Scores` as a matrix, `rocmetrics` assumes that the values in `Scores` are the scores for a multiclass classification problem and uses adjusted score values. A multiclass classification model classifies an observation into a class that yields the largest score, which corresponds to a nonnegative score in the adjusted scores. Therefore, the threshold value is `0`.

• If you specify `Scores` as a column vector, `rocmetrics` assumes that the values in `Scores` are posterior probabilities of the class specified in `ClassNames`. A binary classification model classifies an observation into a class that yields a higher posterior probability, that is, a posterior probability greater than `0.5`. Therefore, the threshold value is `0.5`.

For a binary classification problem, you can specify `Scores` as a two-column matrix or a column vector. However, if the classification scores are not posterior probabilities, you must specify `Scores` as a matrix. A binary classifier classifies an observation into a class that yields a larger score, which is equivalent to a class that yields a nonnegative adjusted score. Therefore, if you specify `Scores` as a matrix for a binary classifier, `rocmetrics` can find a correct model operating point using the same scheme that it applies to a multiclass classifier. If you specify classification scores that are not posterior probabilities as a vector, `rocmetrics` cannot identify a correct model operating point because it always uses `0.5` as a threshold for the model operating point.

The `plot` function displays a filled circle marker at the model operating point for each ROC curve (see `ShowModelOperatingPoint`). The function chooses a point corresponding to the typical threshold value. If the curve does not have a data point for the typical threshold value, the function finds a point that has the smallest threshold value greater than the typical threshold. The point on the curve indicates identical performance to the performance of the typical threshold value.

For an example, see Find Model Operating Point and Optimal Operating Point.

#### Thresholds, Fixed Metric, and Fixed Metric Values

`rocmetrics` finds the ROC curves and other metric values that correspond to the fixed values (`FixedMetricValues` name-value argument) of the fixed metric (`FixedMetric` name-value argument), and stores the values in the `Metrics` property as a table.

The default `FixedMetric` value is `"Thresholds"`, and the default `FixedMetricValues` value is `"all"`. For each class, `rocmetrics` uses all distinct adjusted score values as threshold values, computes the components of the confusion matrix for each threshold value, and then computes performance metrics using the confusion matrix components.

If you use the default `FixedMetricValues` value (`"all"`), specifying a nondefault `FixedMetric` value does not change the software behavior unless you specify to compute confidence intervals. If `rocmetrics` computes confidence intervals, then it holds `FixedMetric` fixed at `FixedMetricValues` and computes confidence intervals for other metrics. For more details, see Pointwise Confidence Intervals.

If you specify a nondefault value for `FixedMetricValues`, `rocmetrics` finds the threshold values corresponding to the specified fixed metric values (`FixedMetricValues` for `FixedMetric`) and computes other performance metric values using the threshold values.

• If you set the `UseNearestNeighbor` name-value argument to `false`, then `rocmetrics` uses the exact threshold values corresponding to the specified fixed metric values.

• If you set `UseNearestNeighbor` to `true`, then among the adjusted scores, `rocmetrics` finds a value that is the nearest to the threshold value corresponding to each specified fixed metric value.

The `Metrics` property includes an additional threshold value that replicates the largest threshold value for each class so that a ROC curve starts from the origin `(0,0)`. The additional threshold value represents the reject-all threshold, for which `TP` = `FP` = `0` (no positive instances, that is, zero true positive instances and zero false positive instances).

Another special threshold in `Metrics` is the accept-all threshold, which is the smallest threshold value for which `TN` = `FN` = `0` (no negative instances, that is, zero true negative instances and zero false negative instances).

Note that the positive predictive value (`PPV = TP/(TP+FP)`) is `NaN` for the reject-all threshold, and the negative predictive value (`NPV = TN/(TN+FN)`) is `NaN` for the accept-all threshold.

#### NaN Score Values

`rocmetrics` processes `NaN` values in the classification score input (`Scores`) in one of two ways:

• If you specify `NaNFlag="omitnan"` (default), then `rocmetrics` discards rows with `NaN` scores.

• If you specify `NaNFlag="includenan"`, then `rocmetrics` adds the instances of `NaN` scores to false classification counts in the respective class for each one-versus-all binary classification. That is, for any threshold, the software counts instances with `NaN` scores from the positive class as false negative (FN), and counts instances with `NaN` scores from the negative class as false positive (FP). The software computes the metrics corresponding to a threshold of `1` by setting the number of true positive (TP) instances to zero and setting the number of true negative (TN) instances to the total count minus the `NaN` count in the negative class.

Consider an example with two rows in the positive class and two rows in the negative class, each pair having a `NaN` score:

True Class LabelClassification Score
Negative0.2
Negative`NaN`
Positive0.7
Positive`NaN`

If you discard rows with `NaN` scores (`NaNFlag="omitnan"`), then as the score threshold varies, `rocmetrics` computes performance metrics as shown in the following table. For example, a threshold of 0.5 corresponds to the middle row where `rocmetrics` classifies rows 1 and 3 correctly and omits rows 2 and 4.

Threshold`TP``FN``FP``TN`
10101
0.51001
01010

If you add rows with `NaN` scores to the false category in their respective classes (`NaNFlag="includenan"`), `rocmetrics` computes performance metrics as shown in the following table. For example, a threshold of 0.5 corresponds to the middle row where `rocmetrics` counts rows 2 and 4 as incorrectly classified. Notice that only the `FN` and `FP` columns differ between these two tables.

Threshold`TP``FN``FP``TN`
10211
0.51111
01120

### Pointwise Confidence Intervals

`rocmetrics` computes pointwise confidence intervals for the performance metrics, including the AUC values and score thresholds, by using either bootstrap samples or cross-validated data. The object stores the values in the `Metrics` and `AUC` properties.

You cannot specify both options. If you specify a custom metric in `AdditionalMetrics`, you must use bootstrap to compute confidence intervals. `rocmetrics` does not support cross-validation for a custom metric.

`rocmetrics` holds `FixedMetric` (threshold, FPR, TPR, or a metric specified in `AdditionalMetrics`) fixed at `FixedMetricValues` and computes the confidence intervals on AUC and other metrics for the points corresponding to the values in `FixedMetricValues`.

• Threshold averaging (TA) (when `FixedMetric` is `"Thresholds"` (default)) — `rocmetrics` estimates confidence intervals for performance metrics at fixed threshold values. The function takes samples at the fixed thresholds and averages the corresponding metric values.

• Vertical averaging (VA) (when `FixedMetric` is a performance metric) — `rocmetrics` estimates confidence intervals for thresholds and other performance metrics at the fixed metric values. The function takes samples at the fixed metric values and averages the corresponding threshold and metric values.

The function estimates confidence intervals for the AUC value only when `FixedMetric` is `"Thresholds"`, `"FalsePositiveRate"`, or `"TruePositiveRate"`.

## References

[1] Fawcett, T. “ROC Graphs: Notes and Practical Considerations for Researchers”, Machine Learning 31, no. 1 (2004): 1–38.

[2] Zweig, M., and G. Campbell. “Receiver-Operating Characteristic (ROC) Plots: A Fundamental Evaluation Tool in Clinical Medicine.” Clinical Chemistry 39, no. 4 (1993): 561–577 .

[3] Davis, J., and M. Goadrich. “The Relationship Between Precision-Recall and ROC Curves.” Proceedings of ICML ’06, 2006, pp. 233–240.

[4] Moskowitz, C. S., and M. S. Pepe. “Quantifying and Comparing the Predictive Accuracy of Continuous Prognostic Factors for Binary Outcomes.” Biostatistics 5, no. 1 (2004): 113–27.

[5] Huang, Y., M. S. Pepe, and Z. Feng. “Evaluating the Predictiveness of a Continuous Marker.” U. Washington Biostatistics Paper Series, 2006, 250–61.

[6] Briggs, W. M., and R. Zaretzki. “The Skill Plot: A Graphical Technique for Evaluating Continuous Diagnostic Tests.” Biometrics 64, no. 1 (2008): 250–256.

[7] Bettinger, R. “Cost-Sensitive Classifier Selection Using the ROC Convex Hull Method.” SAS Institute, 2003.