# classperf

Evaluate classifier performance

## Syntax

## Description

`classperf`

without input arguments displays the properties of a
`classperformance`

object. For more information, see classperformance Properties.

creates an empty `cp`

= classperf(`groundTruth`

)`classperformance`

object `cp`

using
the true labels `groundTruth`

for every observation in your data
set.

creates a `cp`

= classperf(`groundTruth`

,`classifierOutput`

)`classperformance`

object `cp`

using the true
labels `groundTruth`

, and then updates the object properties based on
the results of the classifier `classifierOutput`

. Use this syntax when
you want to know the classifier performance on a single validation run.

`classperf(`

updates the `cp`

,`classifierOutput`

)`classperformance`

object `cp`

with the
results of a classifier `classifierOutput`

. Use this syntax to update
the performance of the classifier iteratively, such as inside a `for`

loop for multiple cross-validation runs.

`classperf(`

uses `cp`

,`classifierOutput`

,`testIdx`

)`testIdx`

to compare the results of the classifier to the true
labels and update the object `cp`

. `testIdx`

represents a subset of the true labels (ground truth) in the current validation.

`classperf(___,`

specifies additional options with one or more `Name,Value`

)`Name,Value`

pair
arguments. Specify these options after all other input arguments.

## Examples

### Perform 10-Fold Cross-Validation

Create indices for the 10-fold cross-validation and classify measurement data for the Fisher iris data set. The Fisher iris data set contains width and length measurements of petals and sepals from three species of irises.

Load the data set.

`load fisheriris`

Create indices for the 10-fold cross-validation.

`indices = crossvalind('Kfold',species,10);`

Initialize an object to measure the performance of the classifier.

cp = classperf(species);

Perform the classification using the measurement data and report the error rate, which is the ratio of the number of incorrectly classified samples divided by the total number of classified samples.

for i = 1:10 test = (indices == i); train = ~test; class = classify(meas(test,:),meas(train,:),species(train,:)); classperf(cp,class,test); end cp.ErrorRate

ans = 0.0200

Suppose you want to use the observation data from the `setosa`

and `virginica`

species only and exclude the `versicolor`

species from cross-validation.

labels = {'setosa','virginica'}; indices = crossvalind('Kfold',species,10,'Classes',labels);

`indices`

now contains zeros for the rows that belong to the `versicolor`

species.

Perform the classification again.

for i = 1:10 test = (indices == i); train = ~test; class = classify(meas(test,:),meas(train,:),species(train,:)); classperf(cp,class,test); end cp.ErrorRate

ans = 0.0160

### Classify Fisher Iris Data Using K-Nearest Neighbor

Load the data set.

```
load fisheriris
X = meas;
Y = species;
```

X is a numeric matrix that contains four petal measurements for 150 irises. Y contains the true class names (labels) of the corresponding iris species.

Initialize the `classperformance`

object using the true labels.

cp = classperf(Y)

cp = classperformance with properties: ClassLabels: {3x1 cell} GroundTruth: [150x1 double] NumberOfObservations: 150 ValidationCounter: 0 SampleDistribution: [150x1 double] ErrorDistribution: [150x1 double] SampleDistributionByClass: [3x1 double] ErrorDistributionByClass: [3x1 double] CountingMatrix: [4x3 double] CorrectRate: NaN ErrorRate: NaN LastCorrectRate: 0 LastErrorRate: 0 InconclusiveRate: NaN ClassifiedRate: NaN Sensitivity: NaN Specificity: NaN PositivePredictiveValue: NaN NegativePredictiveValue: NaN PositiveLikelihood: NaN NegativeLikelihood: NaN Prevalence: NaN DiagnosticTable: [2x2 double] Label: '' Description: '' ControlClasses: [2x1 double] TargetClasses: 1

Perform the classification using the k-nearest neighbor classifier. Cross-validate the model 10 times by using 145 samples as the training set and 5 samples as the test set. After each cross-validation run, update the classifier performance object with the results.

for i = 1:10 [train,test] = crossvalind('LeaveMOut',Y,5); mdl = fitcknn(X(train,:),Y(train),'NumNeighbors',3); predictions = predict(mdl,X(test,:)); classperf(cp,predictions,test); end

Report the classification error rate, which is a ratio of the number of incorrectly classified samples divided by the total number of classified samples.

cp.ErrorRate

ans = 0.0467

## Input Arguments

`groundTruth`

— True labels

vector of integers | logical vector | string vector | cell array of character vectors

True labels for all observations in your data set, specified as a vector of integers, logical vector, string vector, or cell array of character vectors.

`classifierOutput`

— Classification results

vector of integers | logical vector | string vector | cell array of character vectors

Classification results from a classifier, specified as a vector of integers, logical
vector, string vector, or cell array of character vectors. When
`classifierOutput`

is a cell array of character vectors or string
vector, an empty character vector or string represents an inconclusive result. For a
vector of integers, `NaN`

represents an inconclusive result.

If you do not specify

`testIdx`

,`classifierOutput`

must be the same size and data type as`groundTruth`

.If you specify

`testIdx`

as a vector of integers,`classifierOutput`

must have the same number of elements as`testIdx`

. If`testIdx`

is a logical vector, the number of elements in`classifierOutput`

must equal`sum(testIdx)`

.

`cp`

— Classifier performance information

`classperformance`

object

Classifier performance information, specified as a `classperformance`

object. For details, see classperformance Properties.

`testIdx`

— Subset of true labels

vector of integers | logical vector

Subset of true labels (`groundTruth`

), specified as a vector of
integers or logical vector. The `testIdx`

argument indicates a subset
of true labels (from a test set). The function uses `testIdx`

as an
index vector to get a subset of labels from `groundTruth`

, such as
`groundTruth(testIdx)`

.

If

`testIdx`

is a logical vector, its length must equal the total number of observations (`cp.NumberOfObservations`

).If

`testIdx`

is a vector of integers, it cannot contain duplicate integers, and each integer must be greater than`0`

but less than or equal to the total number of observations.

### Name-Value Arguments

Specify optional pairs of arguments as
`Name1=Value1,...,NameN=ValueN`

, where `Name`

is
the argument name and `Value`

is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.

*
Before R2021a, use commas to separate each name and value, and enclose*
`Name`

*in quotes.*

**Example: **```
cp = classperf(groundTruth,classifierOutput,'Positive',[1 2
3])
```

specifies the labels for the target (diseased) classes.

`Positive`

— Labels for target classes

vector of integers | logical vector | string vector | cell array of character vectors

Labels for the target classes, specified as the comma-separated pair consisting of
`'Positive'`

and a vector of integers, logical vector, string
vector, or cell array of character vectors.

If

`groundTruth`

is a vector of integers, the positive label and negative label (specified by the`'Negative'`

name-value pair argument) must be vectors of integers.If

`groundTruth`

is a string vector or cell array of character vectors, the positive label and negative label can be string vectors, cell arrays of character vectors, or vectors of positive integers. The entries must be a subset of

.`grp2idx`

(groundTruth)

By default, the positive label corresponds to the first class returned by
`grp2idx(groundTruth)`

and the negative label corresponds to all
other classes.

The function uses the positive label to set the `TargetClasses`

property of the `cp`

object.

The positive and negative labels are disjoint subsets of
`unique(groundTruth)`

. For example, suppose you have a data set
that contains data from six patients. Five patients have ovarian, lung, prostate,
skin, or brain cancer, and one patient does not have cancer. Then ```
ClassLabels
= {'Ovarian', 'Lung', 'Prostate', 'Skin', 'Brain', 'Healthy'}
```

. You can
test a classifier for lung cancer only by setting the positive label to
`[2]`

and the negative label to `[1 3 4 5 6]`

.
Alternatively, you can test for any type of cancer by setting the positive label to
`[1 2 3 4 5]`

and the negative label to
`[6]`

.

In clinical tests, the function counts inconclusive values (empty character vector
`''`

or `NaN`

) as false negatives to calculate the
specificity and as false positives to calculate the sensitivity. The function dose not
count any tested observation with its true class not within the union of positive
label and negative label. However, if the true class of a tested observation is within
the union but its predicted class is not covered by `groundTruth`

,
the function counts that observation as inconclusive.

**Example: **`'Positive',[1 2]`

`Negative`

— Labels for control classes

vector of integers | logical vector | string vector | cell array of character vectors

Labels for the control classes, specified as the comma-separated pair consisting
of `'Negative'`

and a vector of integers, logical vector, string
vector, or cell array of character vectors.

If

`groundTruth`

is a vector of integers, the positive label and negative label (specified by the`'Negative'`

name-value pair argument) must be vectors of integers.If

`groundTruth`

is a string vector or cell array of character vectors, the positive label and negative label can be string vectors, cell arrays of character vectors, or vectors of positive integers. The entries must be a subset of

.`grp2idx`

(groundTruth)

By default, the positive label corresponds to the first class returned by
`grp2idx(groundTruth)`

and the negative label corresponds to all
other classes.

The function uses the negative label to set the
`ControlClasses`

property of the `cp`

object.
For details on how the function uses the positive and negative labels, see Positive.

**Example: **`'Negative',[3]`

## Version History

**Introduced before R2006a**

## See Also

classperformance Properties | `crossvalind`

| `classify`

| `grp2idx`

## MATLAB 명령

다음 MATLAB 명령에 해당하는 링크를 클릭했습니다.

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