Main Content

Classifier performance information

To view the performance-related information of a classifier, create a
`classperformance`

object by using the `classperf`

function. Use dot notation to access the object properties, such as
`CorrectRate`

, `ErrorRate`

,
`Sensitivity`

, and `Specificity`

.

`Label`

— Name of classifier object`''`

(default) | character vectorName of the classifier object, specified as a character vector. Use dot notation to set this property.

**Example: **
`'cp_kfold'`

**Data Types: **`char`

`Description`

— Description of object`''`

(default) | character vectorDescription of the object, specified as a character vector. Use dot notation to set this property.

**Example: **
`'performance_data_kfold'`

**Data Types: **`char`

`ClassLabels`

— Unique set of true labelsvector of positive integers | cell array of character vectors

This property is read-only.

Unique set of true labels from `groundTruth`

, specified as a
vector of positive integers or cell array of character vectors. This property is
equivalent to the output when you run
`unique(`

.`groundTruth`

)

**Example: **
`{'ovarian','liver','normal'}`

**Data Types: **`double`

| `cell`

`GroundTruth`

— True labels for all observationsvector of positive integers | cell array of character vectors

This property is read-only.

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

**Example: **
`{'ovarian','liver','normal','ovarian','ovarian','liver'}`

**Data Types: **`double`

| `cell`

`NumberOfObservations`

— Number of observationspositive integer

This property is read-only.

Number of observations in your data set, specified as a positive integer.

**Example: **
`200`

**Data Types: **`double`

`ControlClasses`

— Indices to control classes from true labelsvector of positive integers

Indices to the control classes from the true labels
(`ClassLabels`

), specified as a vector of positive integers. This
property indicates the control (or negative) classes in the diagnostic test. By default,
`ControlClasses`

contains all classes other than the first class
returned by `grp2idx(`

.`groundTruth`

)

You can set this property by using dot notation or the `'Negative'`

name-value pair argument with the `classperf`

function.

**Example: **
`[3]`

**Data Types: **`double`

`TargetClasses`

— Indices to target classes from true labels vector of positive integers

Indices to the target classes from the true labels
(`ClassLabels`

), specified as a vector of positive integers. This
property indicates the target (or positive) classes in the diagnostic test. By default,
`TargetClasses`

contains the first class returned by
`grp2idx(`

.`groundTruth`

)

You can set this property by using dot notation or the `'Positive'`

name-value pair argument with the `classperf`

function.

**Example: **
`[1 2]`

**Data Types: **`double`

`SampleDistribution`

— Number of evaluations for each samplenumeric vector

This property is read-only.

Number of evaluations for each sample during the validation, specified as a numeric
vector. For example, if you use resubstitution, `SampleDistribution`

is
a vector of ones and `ValidationCounter`

= 1. If you have a 10-fold
cross-validation, `SampleDistribution`

is also a vector of ones, but
`ValidationCounter`

= 10.

`SampleDistribution`

is useful when performing Monte Carlo
partitions of the test sets, and it can help determine if each sample is tested an equal
number of times.

**Example: **
`[0 0 2 0]`

**Data Types: **`double`

`ErrorDistribution`

— Frequency of misclassification of each samplenumeric vector

This property is read-only.

Frequency of misclassification of each sample, specified as a numeric vector.

**Example: **
`[0 0 1 0]`

**Data Types: **`double`

`SampleDistributionByClass`

— Frequency of true classes during validationnumeric vector

This property is read-only.

Frequency of the true classes during the validation, specified as a numeric vector.

**Example: **
`[10 10 0]`

**Data Types: **`double`

`ErrorDistributionByClass`

— Frequency of errors for each classnumeric vector

This property is read-only.

Frequency of errors for each class during the validation, specified as a numeric vector.

**Example: **
`[0 0 0]`

**Data Types: **`double`

`ValidationCounter`

— Number of validationspositive integer

This property is read-only.

Number of validations, specified as a positive integer.

**Example: **
`10`

**Data Types: **`double`

`CountingMatrix`

— Classification confusion matrixnumeric array

This property is read-only.

Classification confusion matrix, specified as a numeric array. The order of the rows
and columns in the matrix is the same as in `grp2idx(groundTruth)`

.
Columns represent the true classes, and rows represent the classifier prediction. The
last row in `CountingMatrix`

is reserved for counting inconclusive
results.

**Example: **
`[10 0 0;0 10 0; 0 0 0; 0 0 0]`

**Data Types: **`double`

`CorrectRate`

— Correct rate of classifierpositive scalar

This property is read-only.

Correct rate of the classifier, specified as a positive scalar.
`CorrectRate`

is defined as the number of correctly classified
samples divided by the number of classified samples. Inconclusive results are not
counted.

**Example: **`1`

**Data Types: **`double`

`ErrorRate`

— Error rate of classifierpositive scalar

This property is read-only.

Error rate of the classifier, specified as a positive scalar.
`ErrorRate`

is defined as the number of incorrectly classified
samples divided by the number of classified samples. Inconclusive results are not
counted.

**Example: **`0`

**Data Types: **`double`

`LastCorrectRate`

— Correct rate of classifier during last runpositive scalar

This property is read-only.

Correct rate of the classifier during the last validation run, specified as a
positive scalar. In contrast with `CorrectRate`

,
`LastCorrectRate`

only applies to the evaluated samples from the
most recent validation run of the classifier performance object.

**Example: **
`1`

**Data Types: **`double`

`LastErrorRate`

— Error rate of classifier during last validationpositive scalar

This property is read-only.

Error rate of the classifier during the last validation run, specified as a positive
scalar. In contrast with `ErrorRate`

,
`LastErrorRate`

only applies to the evaluated samples from the most
recent validation run of the classifier performance object.

**Example: **
`0`

**Data Types: **`double`

`InconclusiveRate`

— Inconclusive rate of classifierpositive scalar

This property is read-only.

Inconclusive rate of the classifier, specified as a positive scalar.
`InconclusiveRate`

is defined as the number of nonclassified
(inconclusive) samples divided by the total number of samples.

**Example: **
`0`

**Data Types: **`double`

`ClassifiedRate`

— Classified rate of classifierpositive scalar

This property is read-only.

Classified rate of the classifier, specified as a positive scalar.
`ClassifiedRate`

is defined as the number of classified samples
divided by the total number of samples.

**Example: **
`1`

**Data Types: **`double`

`Sensitivity`

— Sensitivity of classifierpositive scalar

This property is read-only.

Sensitivity of the classifier, specified as a positive scalar.
`Sensitivity`

is defined as the number of correctly classified
positive samples divided by the number of true positive samples.

Inconclusive results that are true positives are counted as errors for computing
`Sensitivity`

. In other words, inconclusive results can decrease the
diagnostic value of the test.

**Example: **
`1`

**Data Types: **`double`

`Specificity`

— Specificity of classifierpositive scalar

This property is read-only.

Specificity of the classifier, specified as a positive scalar.
`Specificity`

is defined as the number of correctly classified
negative samples divided by the number of true negative samples.

Inconclusive results that are true negatives are counted as errors for computing
`Specificity`

. In other words, inconclusive results can decrease the
diagnostic value of the test.

**Example: **
`0.8`

**Data Types: **`double`

`PositivePredictiveValue`

— Positive predictive value of classifierpositive scalar

This property is read-only.

Positive predictive value of the classifier, specified as a positive scalar.
`PositivePredictiveValue`

is defined as the number of correctly
classified positive samples divided by the number of positive classified samples.

Inconclusive results are classified as negative when computing
`PositivePredictiveValue`

.

**Example: **
`1`

**Data Types: **`double`

`NegativePredictiveValue`

— Negative predictive value of classifierpositive scalar

This property is read-only.

Negative predictive value of the classifier, specified as a positive scalar.
`NegativePredictiveValue`

is defined as the number of correctly
classified negative samples divided by the number of negative classified samples.

Inconclusive results are classified as positive when computing
`NegativePredictiveValue`

.

**Example: **
`1`

**Data Types: **`double`

`PositiveLikelihood`

— Positive likelihood of classifierpositive scalar

This property is read-only.

Positive likelihood of the classifier, specified as a positive scalar.
`PositiveLikelihood`

is defined as

.`Sensitivity`

/ (1 -
`Specificity`

)

**Example: **`5`

**Data Types: **`double`

`NegativeLikelihood`

— Negative likelihood of classifierpositive scalar

This property is read-only.

Negative likelihood of the classifier, specified as a positive scalar.
`NegativeLikelihood`

is defined as ```
(1 -
```

.`Sensitivity`

)/`Specificity`

**Example: **`0`

**Data Types: **`double`

`Prevalence`

— Prevalence of classifierpositive scalar

This property is read-only.

Prevalence of the classifier, specified as a positive scalar.
`Prevalence`

is defined as the number of true positive samples
divided by the total number of samples.

**Example: **
`1`

**Data Types: **`double`

`DiagnosticTable`

— Diagnostic table2-by-2 numeric array

This property is read-only.

Diagnostic table, specified as a two-by-two numeric array. The first row indicates the number of samples classified as positive, with the number of true positives in the first column and the number of false positives in the second column. The second row indicates the number of samples classified as negative, with the number of false negatives in the first column and the number of true negatives in the second column.

Correct classifications appear in the diagonal elements and errors appear in the off-diagonal elements. Inconclusive results are considered errors and are counted in the off-diagonal elements. For an example, see Diagnostic Table Example.

**Example: **
`[20 0;0 0]`

**Data Types: **`double`

Suppose that a cancer study of 10 patients yields these results.

Patient | Classifier Output | Has Cancer |
---|---|---|

1 | Positive | Yes |

2 | Positive | Yes |

3 | Positive | Yes |

4 | Positive | No |

5 | Negative | Yes |

6 | Negative | No |

7 | Negative | No |

8 | Negative | No |

9 | Negative | No |

10 | Inconclusive | Yes |

Using these results, the function computes the `DiagnosticTable`

as
follows:

`classify`

| `classperf`

| `crossvalind`

| `grp2idx`

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