classperformance Properties
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
.
Name and Description
Label
— Name of classifier object
''
(default) | character vector
Name 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 vector
Description of the object, specified as a character vector. Use dot notation to set this property.
Example:
'performance_data_kfold'
Data Types: char
True Labels and Indices
ClassLabels
— Unique set of true labels
vector 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 observations
vector 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 observations
positive 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 labels
vector 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
Sample and Error Distributions
SampleDistribution
— Number of evaluations for each sample
numeric 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 sample
numeric 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 validation
numeric 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 class
numeric 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
Performance Statistics
ValidationCounter
— Number of validations
positive integer
This property is read-only.
Number of validations, specified as a positive integer.
Example:
10
Data Types: double
CountingMatrix
— Classification confusion matrix
numeric 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 classifier
positive 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 classifier
positive 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 run
positive 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 validation
positive 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 classifier
positive 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 classifier
positive 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 classifier
positive 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 classifier
positive 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 classifier
positive 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 classifier
positive 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 classifier
positive 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 classifier
positive 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 classifier
positive 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 table
2-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
More About
Diagnostic Table Example
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:
Version History
Introduced before R2006a
See Also
classperf
| crossvalind
| classify
| grp2idx
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