LinearSVCSummaryImpl (Spark 3.4.4 JavaDoc)

Object
- org.apache.spark.ml.classification.LinearSVCSummaryImpl

All Implemented Interfaces:

java.io.Serializable, BinaryClassificationSummary, ClassificationSummary, LinearSVCSummary

Direct Known Subclasses:

LinearSVCTrainingSummaryImpl
```
public class LinearSVCSummaryImpl
extends Object
implements LinearSVCSummary
```
LinearSVC results for a given model.
param: predictions dataframe output by the model's transform method. param: scoreCol field in "predictions" which gives the rawPrediction of each instance. param: predictionCol field in "predictions" which gives the prediction for a data instance as a double. param: labelCol field in "predictions" which gives the true label of each instance. param: weightCol field in "predictions" which gives the weight of each instance.

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description
`LinearSVCSummaryImpl(Dataset<Row> predictions, String scoreCol, String predictionCol, String labelCol, String weightCol)`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double`	`areaUnderROC()` Computes the area under the receiver operating characteristic (ROC) curve.
`Dataset<Row>`	`fMeasureByThreshold()` Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.
`String`	`labelCol()` Field in "predictions" which gives the true label of each instance (if available).
`Dataset<Row>`	`pr()` Returns the precision-recall curve, which is a Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.
`Dataset<Row>`	`precisionByThreshold()` Returns a dataframe with two fields (threshold, precision) curve.
`String`	`predictionCol()` Field in "predictions" which gives the prediction of each class.
`Dataset<Row>`	`predictions()` Dataframe output by the model's `transform` method.
`Dataset<Row>`	`recallByThreshold()` Returns a dataframe with two fields (threshold, recall) curve.
`Dataset<Row>`	`roc()` Returns the receiver operating characteristic (ROC) curve, which is a Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
`String`	`scoreCol()` Field in "predictions" which gives the probability or rawPrediction of each class as a vector.
`String`	`weightCol()` Field in "predictions" which gives the weight of each instance.

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

Methods inherited from interface org.apache.spark.ml.classification.ClassificationSummary
accuracy, falsePositiveRateByLabel, fMeasureByLabel, fMeasureByLabel, labels, precisionByLabel, recallByLabel, truePositiveRateByLabel, weightedFalsePositiveRate, weightedFMeasure, weightedFMeasure, weightedPrecision, weightedRecall, weightedTruePositiveRate

- Constructor Detail
  - LinearSVCSummaryImpl
```
public LinearSVCSummaryImpl(Dataset<Row> predictions,
                            String scoreCol,
                            String predictionCol,
                            String labelCol,
                            String weightCol)
```
- Method Detail
  - areaUnderROC
```
public double areaUnderROC()
```
    Description copied from interface: BinaryClassificationSummary
    
    Computes the area under the receiver operating characteristic (ROC) curve.
    
    Specified by:
    
    areaUnderROC in interface BinaryClassificationSummary
    
    Returns:
    
    (undocumented)
  - fMeasureByThreshold
```
public Dataset<Row> fMeasureByThreshold()
```
    Description copied from interface: BinaryClassificationSummary
    
    Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.
    
    Specified by:
    
    fMeasureByThreshold in interface BinaryClassificationSummary
    
    Returns:
    
    (undocumented)
  - labelCol
```
public String labelCol()
```
    Description copied from interface: ClassificationSummary
    
    Field in "predictions" which gives the true label of each instance (if available).
    
    Specified by:
    
    labelCol in interface ClassificationSummary
  - pr
```
public Dataset<Row> pr()
```
    Description copied from interface: BinaryClassificationSummary
    
    Returns the precision-recall curve, which is a Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.
    
    Specified by:
    
    pr in interface BinaryClassificationSummary
    
    Returns:
    
    (undocumented)
  - precisionByThreshold
```
public Dataset<Row> precisionByThreshold()
```
    Description copied from interface: BinaryClassificationSummary
    
    Returns a dataframe with two fields (threshold, precision) curve. Every possible probability obtained in transforming the dataset are used as thresholds used in calculating the precision.
    
    Specified by:
    
    precisionByThreshold in interface BinaryClassificationSummary
    
    Returns:
    
    (undocumented)
  - predictionCol
```
public String predictionCol()
```
    Description copied from interface: ClassificationSummary
    
    Field in "predictions" which gives the prediction of each class.
    
    Specified by:
    
    predictionCol in interface ClassificationSummary
  - predictions
```
public Dataset<Row> predictions()
```
    Description copied from interface: ClassificationSummary
    
    Dataframe output by the model's transform method.
    
    Specified by:
    
    predictions in interface ClassificationSummary
    
    Returns:
    
    (undocumented)
  - recallByThreshold
```
public Dataset<Row> recallByThreshold()
```
    Description copied from interface: BinaryClassificationSummary
    
    Returns a dataframe with two fields (threshold, recall) curve. Every possible probability obtained in transforming the dataset are used as thresholds used in calculating the recall.
    
    Specified by:
    
    recallByThreshold in interface BinaryClassificationSummary
    
    Returns:
    
    (undocumented)
  - roc
```
public Dataset<Row> roc()
```
    Description copied from interface: BinaryClassificationSummary
    
    Returns the receiver operating characteristic (ROC) curve, which is a Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it. See http://en.wikipedia.org/wiki/Receiver_operating_characteristic
    
    Specified by:
    
    roc in interface BinaryClassificationSummary
    
    Returns:
    
    (undocumented)
  - scoreCol
```
public String scoreCol()
```
    Description copied from interface: BinaryClassificationSummary
    
    Field in "predictions" which gives the probability or rawPrediction of each class as a vector.
    
    Specified by:
    
    scoreCol in interface BinaryClassificationSummary
    
    Returns:
    
    (undocumented)
  - weightCol
```
public String weightCol()
```
    Description copied from interface: ClassificationSummary
    
    Field in "predictions" which gives the weight of each instance.
    
    Specified by:
    
    weightCol in interface ClassificationSummary

Class LinearSVCSummaryImpl

Constructor Summary

Method Summary

Methods inherited from class Object

Methods inherited from interface org.apache.spark.ml.classification.ClassificationSummary

Constructor Detail

LinearSVCSummaryImpl

Method Detail

areaUnderROC

fMeasureByThreshold

labelCol

pr

precisionByThreshold

predictionCol

predictions

recallByThreshold

roc

scoreCol

weightCol