MulticlassMetrics (Spark 3.5.3 JavaDoc)

Object
- org.apache.spark.mllib.evaluation.MulticlassMetrics

```
public class MulticlassMetrics
extends Object
```
Evaluator for multiclass classification.
param: predictionAndLabels an RDD of (prediction, label, weight, probability) or (prediction, label, weight) or (prediction, label) tuples.

Constructor Summary

Constructors
Constructor and Description

MulticlassMetrics(RDD<? extends scala.Product> predictionAndLabels)

Constructors
Constructor and Description
`MulticlassMetrics(RDD<? extends scala.Product> predictionAndLabels)`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double`	`accuracy()`
`Matrix`	`confusionMatrix()` Returns confusion matrix: predicted classes are in columns, they are ordered by class label ascending, as in "labels"
`double`	`falsePositiveRate(double label)` Returns false positive rate for a given label (category)
`double`	`fMeasure(double label)` Returns f1-measure for a given label (category)
`double`	`fMeasure(double label, double beta)` Returns f-measure for a given label (category)
`double`	`hammingLoss()`
`double[]`	`labels()`
`double`	`logLoss(double eps)` Returns the log-loss, aka logistic loss or cross-entropy loss.
`double`	`precision(double label)` Returns precision for a given label (category)
`double`	`recall(double label)` Returns recall for a given label (category)
`double`	`truePositiveRate(double label)` Returns true positive rate for a given label (category)
`double`	`weightedFalsePositiveRate()`
`double`	`weightedFMeasure()`
`double`	`weightedFMeasure(double beta)` Returns weighted averaged f-measure
`double`	`weightedPrecision()`
`double`	`weightedRecall()`
`double`	`weightedTruePositiveRate()`

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - MulticlassMetrics
```
public MulticlassMetrics(RDD<? extends scala.Product> predictionAndLabels)
```
- Method Detail
  - accuracy
```
public double accuracy()
```
  - confusionMatrix
```
public Matrix confusionMatrix()
```
    Returns confusion matrix: predicted classes are in columns, they are ordered by class label ascending, as in "labels"
    
    Returns:
    
    (undocumented)
  - fMeasure
```
public double fMeasure(double label,
                       double beta)
```
    Returns f-measure for a given label (category)
    
    Parameters:
    
    label - the label.
    
    beta - the beta parameter.
    
    Returns:
    
    (undocumented)
  - fMeasure
```
public double fMeasure(double label)
```
    Returns f1-measure for a given label (category)
    
    Parameters:
    
    label - the label.
    
    Returns:
    
    (undocumented)
  - falsePositiveRate
```
public double falsePositiveRate(double label)
```
    Returns false positive rate for a given label (category)
    
    Parameters:
    
    label - the label.
    
    Returns:
    
    (undocumented)
  - hammingLoss
```
public double hammingLoss()
```
  - labels
```
public double[] labels()
```
  - logLoss
```
public double logLoss(double eps)
```
    Returns the log-loss, aka logistic loss or cross-entropy loss.
    
    Parameters:
    
    eps - log-loss is undefined for p=0 or p=1, so probabilities are clipped to max(eps, min(1 - eps, p)).
    
    Returns:
    
    (undocumented)
  - precision
```
public double precision(double label)
```
    Returns precision for a given label (category)
    
    Parameters:
    
    label - the label.
    
    Returns:
    
    (undocumented)
  - recall
```
public double recall(double label)
```
    Returns recall for a given label (category)
    
    Parameters:
    
    label - the label.
    
    Returns:
    
    (undocumented)
  - truePositiveRate
```
public double truePositiveRate(double label)
```
    Returns true positive rate for a given label (category)
    
    Parameters:
    
    label - the label.
    
    Returns:
    
    (undocumented)
  - weightedFMeasure
```
public double weightedFMeasure(double beta)
```
    Returns weighted averaged f-measure
    
    Parameters:
    
    beta - the beta parameter.
    
    Returns:
    
    (undocumented)
  - weightedFMeasure
```
public double weightedFMeasure()
```
  - weightedFalsePositiveRate
```
public double weightedFalsePositiveRate()
```
  - weightedPrecision
```
public double weightedPrecision()
```
  - weightedRecall
```
public double weightedRecall()
```
  - weightedTruePositiveRate
```
public double weightedTruePositiveRate()
```

Class MulticlassMetrics

Constructor Summary

Method Summary

Methods inherited from class Object

Constructor Detail

MulticlassMetrics

Method Detail

accuracy

confusionMatrix

fMeasure

fMeasure

falsePositiveRate

hammingLoss

labels

logLoss

precision

recall

truePositiveRate

weightedFMeasure

weightedFMeasure

weightedFalsePositiveRate

weightedPrecision

weightedRecall

weightedTruePositiveRate