ClassificationMetrics (Core 2.96 API)

java.lang.Object
- org.neuroph.eval.classification.ClassificationMetrics

```
public final class ClassificationMetrics
extends Object
```
Container class for all metrics which use confusion matrix for their computation Based on: http://java-ml.sourceforge.net/api/0.1.7/net/sf/javaml/classification/evaluation/PerformanceMeasure.html http://sourceforge.net/p/java-ml/java-ml-code/ci/a25ddde7c3677da44e47a643f88e32e2c8bbc32f/tree/net/sf/javaml/classification/evaluation/PerformanceMeasure.java http://en.wikipedia.org/wiki/Matthews_correlation_coefficient

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class ClassificationMetrics.Stats

Nested Classes
Modifier and Type	Class and Description
`static class`	`ClassificationMetrics.Stats`

Constructor Summary

Constructors
Constructor and Description
`ClassificationMetrics(int truePositive, int trueNegative, int falsePositive, int falseNegative)` Constructs a new measure using arguments TODO: add class to which measure corresponds?

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`static ClassificationMetrics.Stats`	`average(ClassificationMetrics[] results)`
`static ClassificationMetrics[]`	`createFromMatrix(ConfusionMatrix confusionMatrix)`
`double`	`getAccuracy()` Calculate and return classification accuracy measure.
`double`	`getBalancedClassificationRate()`
`String`	`getClassLabel()` Returns class label for
`double`	`getErrorRate()` A number of wrong predictions made divided by the total number of predictions made.
`double`	`getFalseDiscoveryRate()`
`double`	`getFalseNegativeRate()`
`double`	`getFalsePositiveRate()`
`double`	`getFMeasure()` Calculates F-score for beta equal to 1.
`double`	`getFMeasure(int beta)` Returns the F-score.
`double`	`getMatthewsCorrelationCoefficient()`
`double`	`getPrecision()` Calculate and return classification precision measure.
`double`	`getQ9()`
`double`	`getRecall()`
`double`	`getSensitivity()` Calculate and return classification sensitivity (recall, true positive rate) number of correctly classified positive examples divided by the total number of actual positive examples
`double`	`getSpecificity()` Specifity , true negative rate
`double`	`getTotal()` Returns total number of classifications.
`void`	`setClassLabel(String classLabel)`
`String`	`toString()`

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - ClassificationMetrics
```
public ClassificationMetrics(int truePositive,
                             int trueNegative,
                             int falsePositive,
                             int falseNegative)
```
    Constructs a new measure using arguments TODO: add class to which measure corresponds?
    
    Parameters:
    
    truePositive -
    
    trueNegative -
    
    falsePositive -
    
    falseNegative -
- Method Detail
  - getClassLabel
```
public String getClassLabel()
```
    Returns class label for
    
    Returns:
    
    class labels
  - setClassLabel
```
public void setClassLabel(String classLabel)
```
  - getAccuracy
```
public double getAccuracy()
```
    Calculate and return classification accuracy measure. A number of correct predictions made divided by the total number of predictions made a = ( tp + tn ) / n
    
    Returns:
    
    classification accuracy
  - getErrorRate
```
public double getErrorRate()
```
    A number of wrong predictions made divided by the total number of predictions made. Also error = 1 - accuracy
    
    Returns:
  - getPrecision
```
public double getPrecision()
```
    Calculate and return classification precision measure. A number of correctly classified positive examples divided by the total number of examples that are classified as positive Also known as positive predictive value PPV
    
    Returns:
    
    classification precision measure
  - getSensitivity
```
public double getSensitivity()
```
    Calculate and return classification sensitivity (recall, true positive rate) number of correctly classified positive examples divided by the total number of actual positive examples
    
    Returns:
  - getRecall
```
public double getRecall()
```
  - getSpecificity
```
public double getSpecificity()
```
    Specifity , true negative rate
  - getTotal
```
public double getTotal()
```
    Returns total number of classifications.
    
    Returns:
    
    total number of classifications
  - getFalsePositiveRate
```
public double getFalsePositiveRate()
```
  - getFalseNegativeRate
```
public double getFalseNegativeRate()
```
  - getFalseDiscoveryRate
```
public double getFalseDiscoveryRate()
```
  - getMatthewsCorrelationCoefficient
```
public double getMatthewsCorrelationCoefficient()
```
  - getFMeasure
```
public double getFMeasure()
```
    Calculates F-score for beta equal to 1.
    
    Returns:
    
    f-score
  - getFMeasure
```
public double getFMeasure(int beta)
```
    Returns the F-score. When recall and precision are zero, this method will return 0.
    
    Parameters:
    
    beta -
    
    Returns:
    
    f-score
  - getQ9
```
public double getQ9()
```
  - getBalancedClassificationRate
```
public double getBalancedClassificationRate()
```
  - toString
```
public String toString()
```
    Overrides:
    
    toString in class Object
  - createFromMatrix
```
public static ClassificationMetrics[] createFromMatrix(ConfusionMatrix confusionMatrix)
```
  - average
```
public static ClassificationMetrics.Stats average(ClassificationMetrics[] results)
```
    Parameters:
    
    results - list of different metric results computed on different sets of data
    
    Returns:
    
    average metrics computed different MetricResults

Class ClassificationMetrics

Nested Class Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

ClassificationMetrics

Method Detail

getClassLabel

setClassLabel

getAccuracy

getErrorRate

getPrecision

getSensitivity

getRecall

getSpecificity

getTotal

getFalsePositiveRate

getFalseNegativeRate

getFalseDiscoveryRate

getMatthewsCorrelationCoefficient

getFMeasure

getFMeasure

getQ9

getBalancedClassificationRate

toString

createFromMatrix

average