BinaryClassificationSummary (Spark 3.5.5 JavaDoc) (original) (raw)

• All Superinterfaces:
  ClassificationSummary, java.io.Serializable
  All Known Subinterfaces:
  BinaryLogisticRegressionSummary, BinaryLogisticRegressionTrainingSummary, BinaryRandomForestClassificationSummary, BinaryRandomForestClassificationTrainingSummary, FMClassificationSummary, FMClassificationTrainingSummary, LinearSVCSummary, LinearSVCTrainingSummary
  All Known Implementing Classes:
  BinaryLogisticRegressionSummaryImpl, BinaryLogisticRegressionTrainingSummaryImpl, BinaryRandomForestClassificationSummaryImpl, BinaryRandomForestClassificationTrainingSummaryImpl, FMClassificationSummaryImpl, FMClassificationTrainingSummaryImpl, LinearSVCSummaryImpl, LinearSVCTrainingSummaryImpl

public interface BinaryClassificationSummary
extends ClassificationSummary
Abstraction for binary classification results for a given model.

• • Method Summary

  All Methods Instance Methods Abstract 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.
  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.
  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.

     * ### 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-), [labelCol](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#labelCol--), [labels](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#labels--), [precisionByLabel](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#precisionByLabel--), [predictionCol](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#predictionCol--), [predictions](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#predictions--), [recallByLabel](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#recallByLabel--), [truePositiveRateByLabel](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#truePositiveRateByLabel--), [weightCol](../../../../../org/apache/spark/ml/classification/ClassificationSummary.html#weightCol--), [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--)`

• • Method Detail

     * #### areaUnderROC  
     double areaUnderROC()  
     Computes the area under the receiver operating characteristic (ROC) curve.  
     Returns:  
     (undocumented)  
     * #### fMeasureByThreshold  
     [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.  
     Returns:  
     (undocumented)  
     * #### pr  
     [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.  
     Returns:  
     (undocumented)  
     * #### precisionByThreshold  
     [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.  
     Returns:  
     (undocumented)  
     * #### recallByThreshold  
     [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.  
     Returns:  
     (undocumented)  
     * #### roc  
     [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  
     Returns:  
     (undocumented)  
     * #### scoreCol  
     String scoreCol()  
     Field in "predictions" which gives the probability or rawPrediction of each class as a vector.  
     Returns:  
     (undocumented)