ClassificationSummary (Spark 3.5.5 JavaDoc) (original) (raw)

• All Superinterfaces:
  java.io.Serializable
  All Known Subinterfaces:
  BinaryClassificationSummary, BinaryLogisticRegressionSummary, BinaryLogisticRegressionTrainingSummary, BinaryRandomForestClassificationSummary, BinaryRandomForestClassificationTrainingSummary, FMClassificationSummary, FMClassificationTrainingSummary, LinearSVCSummary, LinearSVCTrainingSummary, LogisticRegressionSummary, LogisticRegressionTrainingSummary, MultilayerPerceptronClassificationSummary, MultilayerPerceptronClassificationTrainingSummary, RandomForestClassificationSummary, RandomForestClassificationTrainingSummary
  All Known Implementing Classes:
  BinaryLogisticRegressionSummaryImpl, BinaryLogisticRegressionTrainingSummaryImpl, BinaryRandomForestClassificationSummaryImpl, BinaryRandomForestClassificationTrainingSummaryImpl, FMClassificationSummaryImpl, FMClassificationTrainingSummaryImpl, LinearSVCSummaryImpl, LinearSVCTrainingSummaryImpl, LogisticRegressionSummaryImpl, LogisticRegressionTrainingSummaryImpl, MultilayerPerceptronClassificationSummaryImpl, MultilayerPerceptronClassificationTrainingSummaryImpl, RandomForestClassificationSummaryImpl, RandomForestClassificationTrainingSummaryImpl

public interface ClassificationSummary
extends scala.Serializable
Abstraction for multiclass classification results for a given model.

• • Method Summary

  All Methods Instance Methods Abstract Methods

  Modifier and Type	Method and Description
  double	accuracy() Returns accuracy.
  double[]	falsePositiveRateByLabel() Returns false positive rate for each label (category).
  double[]	fMeasureByLabel() Returns f1-measure for each label (category).
  double[]	fMeasureByLabel(double beta) Returns f-measure for each label (category).
  String	labelCol() Field in "predictions" which gives the true label of each instance (if available).
  double[]	labels() Returns the sequence of labels in ascending order.
  double[]	precisionByLabel() Returns precision for each label (category).
  String	predictionCol() Field in "predictions" which gives the prediction of each class.
  Dataset<Row>	predictions() Dataframe output by the model's transform method.
  double[]	recallByLabel() Returns recall for each label (category).
  double[]	truePositiveRateByLabel() Returns true positive rate for each label (category).
  String	weightCol() Field in "predictions" which gives the weight of each instance.
  double	weightedFalsePositiveRate() Returns weighted false positive rate.
  double	weightedFMeasure() Returns weighted averaged f1-measure.
  double	weightedFMeasure(double beta) Returns weighted averaged f-measure.
  double	weightedPrecision() Returns weighted averaged precision.
  double	weightedRecall() Returns weighted averaged recall.
  double	weightedTruePositiveRate() Returns weighted true positive rate.

• • Method Detail

     * #### accuracy  
     double accuracy()  
     Returns accuracy. (equals to the total number of correctly classified instances out of the total number of instances.)  
     Returns:  
     (undocumented)  
     * #### fMeasureByLabel  
     double[] fMeasureByLabel(double beta)  
     Returns f-measure for each label (category).  
     * #### fMeasureByLabel  
     double[] fMeasureByLabel()  
     Returns f1-measure for each label (category).  
     * #### falsePositiveRateByLabel  
     double[] falsePositiveRateByLabel()  
     Returns false positive rate for each label (category).  
     * #### labelCol  
     String labelCol()  
     Field in "predictions" which gives the true label of each instance (if available).  
     * #### labels  
     double[] labels()  
     Returns the sequence of labels in ascending order. This order matches the order used in metrics which are specified as arrays over labels, e.g., truePositiveRateByLabel.  
      Note: In most cases, it will be values {0.0, 1.0, ..., numClasses-1}, However, if the training set is missing a label, then all of the arrays over labels (e.g., from truePositiveRateByLabel) will be of length numClasses-1 instead of the expected numClasses.  
     Returns:  
     (undocumented)  
     * #### precisionByLabel  
     double[] precisionByLabel()  
     Returns precision for each label (category).  
     * #### predictionCol  
     String predictionCol()  
     Field in "predictions" which gives the prediction of each class.  
     * #### predictions  
     [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.  
     Returns:  
     (undocumented)  
     * #### recallByLabel  
     double[] recallByLabel()  
     Returns recall for each label (category).  
     * #### truePositiveRateByLabel  
     double[] truePositiveRateByLabel()  
     Returns true positive rate for each label (category).  
     * #### weightCol  
     String weightCol()  
     Field in "predictions" which gives the weight of each instance.  
     * #### weightedFMeasure  
     double weightedFMeasure(double beta)  
     Returns weighted averaged f-measure.  
     * #### weightedFMeasure  
     double weightedFMeasure()  
     Returns weighted averaged f1-measure.  
     * #### weightedFalsePositiveRate  
     double weightedFalsePositiveRate()  
     Returns weighted false positive rate.  
     * #### weightedPrecision  
     double weightedPrecision()  
     Returns weighted averaged precision.  
     * #### weightedRecall  
     double weightedRecall()  
     Returns weighted averaged recall. (equals to precision, recall and f-measure)  
     Returns:  
     (undocumented)  
     * #### weightedTruePositiveRate  
     double weightedTruePositiveRate()  
     Returns weighted true positive rate. (equals to precision, recall and f-measure)  
     Returns:  
     (undocumented)