LinearSVCSummaryImpl (Spark 3.5.5 JavaDoc) (original) (raw)

• 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](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html "interface in org.apache.spark.ml.classification")  
     `[accuracy](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#accuracy--), [falsePositiveRateByLabel](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#falsePositiveRateByLabel--), [fMeasureByLabel](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#fMeasureByLabel--), [fMeasureByLabel](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#fMeasureByLabel-double-), [labels](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#labels--), [precisionByLabel](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#precisionByLabel--), [recallByLabel](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#recallByLabel--), [truePositiveRateByLabel](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#truePositiveRateByLabel--), [weightedFalsePositiveRate](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#weightedFalsePositiveRate--), [weightedFMeasure](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#weightedFMeasure--), [weightedFMeasure](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#weightedFMeasure-double-), [weightedPrecision](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#weightedPrecision--), [weightedRecall](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#weightedRecall--), [weightedTruePositiveRate](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#weightedTruePositiveRate--)`

• • Constructor Detail

     * #### LinearSVCSummaryImpl  
     public LinearSVCSummaryImpl([Dataset](../../../../../org/apache/spark/sql/Dataset.html "class in org.apache.spark.sql")<[Row](../../../../../org/apache/spark/sql/Row.html "interface in org.apache.spark.sql")> predictions,  
                                 String scoreCol,  
                                 String predictionCol,  
                                 String labelCol,  
                                 String weightCol)  

  • Method Detail

    * #### areaUnderROC  
    public double areaUnderROC()  
    Computes the area under the receiver operating characteristic (ROC) curve.  
    Specified by:  
    `[areaUnderROC](../../../../../org/apache/spark/ml/classification/BinaryClassificationSummary.html#areaUnderROC--)` in interface `[BinaryClassificationSummary](../../../../../org/apache/spark/ml/classification/BinaryClassificationSummary.html "interface in org.apache.spark.ml.classification")`  
    Returns:  
    (undocumented)  
    * #### fMeasureByThreshold  
    public [Dataset](../../../../../org/apache/spark/sql/Dataset.html "class in org.apache.spark.sql")<[Row](../../../../../org/apache/spark/sql/Row.html "interface in org.apache.spark.sql")> fMeasureByThreshold()  
    Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.  
    Specified by:  
    `[fMeasureByThreshold](../../../../../org/apache/spark/ml/classification/BinaryClassificationSummary.html#fMeasureByThreshold--)` in interface `[BinaryClassificationSummary](../../../../../org/apache/spark/ml/classification/BinaryClassificationSummary.html "interface in org.apache.spark.ml.classification")`  
    Returns:  
    (undocumented)  
    * #### labelCol  
    public String labelCol()  
    Field in "predictions" which gives the true label of each instance (if available).  
    Specified by:  
    `[labelCol](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#labelCol--)` in interface `[ClassificationSummary](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html "interface in org.apache.spark.ml.classification")`  
    * #### pr  
    public [Dataset](../../../../../org/apache/spark/sql/Dataset.html "class in org.apache.spark.sql")<[Row](../../../../../org/apache/spark/sql/Row.html "interface in org.apache.spark.sql")> pr()  
    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](../../../../../org/apache/spark/ml/classification/BinaryClassificationSummary.html#pr--)` in interface `[BinaryClassificationSummary](../../../../../org/apache/spark/ml/classification/BinaryClassificationSummary.html "interface in org.apache.spark.ml.classification")`  
    Returns:  
    (undocumented)  
    * #### precisionByThreshold  
    public [Dataset](../../../../../org/apache/spark/sql/Dataset.html "class in org.apache.spark.sql")<[Row](../../../../../org/apache/spark/sql/Row.html "interface in org.apache.spark.sql")> precisionByThreshold()  
    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](../../../../../org/apache/spark/ml/classification/BinaryClassificationSummary.html#precisionByThreshold--)` in interface `[BinaryClassificationSummary](../../../../../org/apache/spark/ml/classification/BinaryClassificationSummary.html "interface in org.apache.spark.ml.classification")`  
    Returns:  
    (undocumented)  
    * #### predictionCol  
    public String predictionCol()  
    Field in "predictions" which gives the prediction of each class.  
    Specified by:  
    `[predictionCol](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#predictionCol--)` in interface `[ClassificationSummary](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html "interface in org.apache.spark.ml.classification")`  
    * #### predictions  
    public [Dataset](../../../../../org/apache/spark/sql/Dataset.html "class in org.apache.spark.sql")<[Row](../../../../../org/apache/spark/sql/Row.html "interface in org.apache.spark.sql")> predictions()  
    Dataframe output by the model's `transform` method.  
    Specified by:  
    `[predictions](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#predictions--)` in interface `[ClassificationSummary](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html "interface in org.apache.spark.ml.classification")`  
    Returns:  
    (undocumented)  
    * #### recallByThreshold  
    public [Dataset](../../../../../org/apache/spark/sql/Dataset.html "class in org.apache.spark.sql")<[Row](../../../../../org/apache/spark/sql/Row.html "interface in org.apache.spark.sql")> recallByThreshold()  
    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](../../../../../org/apache/spark/ml/classification/BinaryClassificationSummary.html#recallByThreshold--)` in interface `[BinaryClassificationSummary](../../../../../org/apache/spark/ml/classification/BinaryClassificationSummary.html "interface in org.apache.spark.ml.classification")`  
    Returns:  
    (undocumented)  
    * #### roc  
    public [Dataset](../../../../../org/apache/spark/sql/Dataset.html "class in org.apache.spark.sql")<[Row](../../../../../org/apache/spark/sql/Row.html "interface in org.apache.spark.sql")> 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. See http://en.wikipedia.org/wiki/Receiver\_operating\_characteristic  
    Specified by:  
    `[roc](../../../../../org/apache/spark/ml/classification/BinaryClassificationSummary.html#roc--)` in interface `[BinaryClassificationSummary](../../../../../org/apache/spark/ml/classification/BinaryClassificationSummary.html "interface in org.apache.spark.ml.classification")`  
    Returns:  
    (undocumented)  
    * #### scoreCol  
    public String scoreCol()  
    Field in "predictions" which gives the probability or rawPrediction of each class as a vector.  
    Specified by:  
    `[scoreCol](../../../../../org/apache/spark/ml/classification/BinaryClassificationSummary.html#scoreCol--)` in interface `[BinaryClassificationSummary](../../../../../org/apache/spark/ml/classification/BinaryClassificationSummary.html "interface in org.apache.spark.ml.classification")`  
    Returns:  
    (undocumented)  
    * #### weightCol  
    public String weightCol()  
    Field in "predictions" which gives the weight of each instance.  
    Specified by:  
    `[weightCol](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#weightCol--)` in interface `[ClassificationSummary](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html "interface in org.apache.spark.ml.classification")`