BinaryLogisticRegressionSummaryImpl (Spark 3.5.5 JavaDoc) (original) (raw)

• Object

• • org.apache.spark.ml.classification.LogisticRegressionSummaryImpl
  • • org.apache.spark.ml.classification.BinaryLogisticRegressionSummaryImpl

• All Implemented Interfaces:
  java.io.Serializable, BinaryClassificationSummary, BinaryLogisticRegressionSummary, ClassificationSummary, LogisticRegressionSummary
  Direct Known Subclasses:
  BinaryLogisticRegressionTrainingSummaryImpl

public class BinaryLogisticRegressionSummaryImpl
extends LogisticRegressionSummaryImpl
implements BinaryLogisticRegressionSummary
Binary logistic regression results for a given model.
param: predictions dataframe output by the model's transform method. param: probabilityCol field in "predictions" which gives the probability of each class as a vector. param: predictionCol field in "predictions" which gives the prediction of each class as a double. param: labelCol field in "predictions" which gives the true label of each instance. param: featuresCol field in "predictions" which gives the features of each instance as a vector. param: weightCol field in "predictions" which gives the weight of each instance.
See Also:
Serialized Form

• • Constructor Summary

  Constructors

  Constructor and Description
  BinaryLogisticRegressionSummaryImpl(Dataset<Row> predictions, String probabilityCol, String predictionCol, String labelCol, String featuresCol, 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.
  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.

     * ### Methods inherited from class org.apache.spark.ml.classification.[LogisticRegressionSummaryImpl](../../../../../org/apache/spark/ml/classification/LogisticRegressionSummaryImpl.html "class in org.apache.spark.ml.classification")  
     `[featuresCol](../../../../../org/apache/spark/ml/classification/LogisticRegressionSummaryImpl.html#featuresCol--), [labelCol](../../../../../org/apache/spark/ml/classification/LogisticRegressionSummaryImpl.html#labelCol--), [predictionCol](../../../../../org/apache/spark/ml/classification/LogisticRegressionSummaryImpl.html#predictionCol--), [predictions](../../../../../org/apache/spark/ml/classification/LogisticRegressionSummaryImpl.html#predictions--), [probabilityCol](../../../../../org/apache/spark/ml/classification/LogisticRegressionSummaryImpl.html#probabilityCol--), [weightCol](../../../../../org/apache/spark/ml/classification/LogisticRegressionSummaryImpl.html#weightCol--)`  
     * ### Methods inherited from class Object  
     `equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`  
     * ### Methods inherited from interface org.apache.spark.ml.classification.[BinaryLogisticRegressionSummary](../../../../../org/apache/spark/ml/classification/BinaryLogisticRegressionSummary.html "interface in org.apache.spark.ml.classification")  
     `[scoreCol](../../../../../org/apache/spark/ml/classification/BinaryLogisticRegressionSummary.html#scoreCol--)`  
     * ### Methods inherited from interface org.apache.spark.ml.classification.[LogisticRegressionSummary](../../../../../org/apache/spark/ml/classification/LogisticRegressionSummary.html "interface in org.apache.spark.ml.classification")  
     `[asBinary](../../../../../org/apache/spark/ml/classification/LogisticRegressionSummary.html#asBinary--), [featuresCol](../../../../../org/apache/spark/ml/classification/LogisticRegressionSummary.html#featuresCol--), [probabilityCol](../../../../../org/apache/spark/ml/classification/LogisticRegressionSummary.html#probabilityCol--)`  
     * ### 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--)`

• • Constructor Detail

     * #### BinaryLogisticRegressionSummaryImpl  
     public BinaryLogisticRegressionSummaryImpl([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 probabilityCol,  
                                                String predictionCol,  
                                                String labelCol,  
                                                String featuresCol,  
                                                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)  
    * #### 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)  
    * #### 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)