Packages

t

org.apache.spark.ml.classification

LogisticRegressionSummary

sealed trait LogisticRegressionSummary extends Serializable

Abstraction for logistic regression results for a given model.

Currently, the summary ignores the instance weights.

Source
LogisticRegression.scala
Linear Supertypes
Serializable, Serializable, AnyRef, Any
Known Subclasses
BinaryLogisticRegressionSummary, BinaryLogisticRegressionTrainingSummary, LogisticRegressionTrainingSummary
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. LogisticRegressionSummary
  2. Serializable
  3. Serializable
  4. AnyRef
  5. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Abstract Value Members

  1. abstract def featuresCol: String

    Field in "predictions" which gives the features of each instance as a vector.

    Field in "predictions" which gives the features of each instance as a vector.

    Annotations
    @Since( "1.6.0" )
  2. abstract def labelCol: String

    Field in "predictions" which gives the true label of each instance (if available).

    Field in "predictions" which gives the true label of each instance (if available).

    Annotations
    @Since( "1.5.0" )
  3. abstract def predictionCol: String

    Field in "predictions" which gives the prediction of each class.

    Field in "predictions" which gives the prediction of each class.

    Annotations
    @Since( "2.3.0" )
  4. abstract def predictions: DataFrame

    Dataframe output by the model's transform method.

    Dataframe output by the model's transform method.

    Annotations
    @Since( "1.5.0" )
  5. abstract def probabilityCol: String

    Field in "predictions" which gives the probability of each class as a vector.

    Field in "predictions" which gives the probability of each class as a vector.

    Annotations
    @Since( "1.5.0" )

Concrete Value Members

  1. def accuracy: Double

    Returns accuracy.

    Returns accuracy. (equals to the total number of correctly classified instances out of the total number of instances.)

    Annotations
    @Since( "2.3.0" )
  2. def asBinary: BinaryLogisticRegressionSummary

    Convenient method for casting to binary logistic regression summary.

    Convenient method for casting to binary logistic regression summary. This method will throw an Exception if the summary is not a binary summary.

    Annotations
    @Since( "2.3.0" )
  3. def fMeasureByLabel: Array[Double]

    Returns f1-measure for each label (category).

    Returns f1-measure for each label (category).

    Annotations
    @Since( "2.3.0" )
  4. def fMeasureByLabel(beta: Double): Array[Double]

    Returns f-measure for each label (category).

    Returns f-measure for each label (category).

    Annotations
    @Since( "2.3.0" )
  5. def falsePositiveRateByLabel: Array[Double]

    Returns false positive rate for each label (category).

    Returns false positive rate for each label (category).

    Annotations
    @Since( "2.3.0" )
  6. def labels: Array[Double]

    Returns the sequence of labels in ascending order.

    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.

    Annotations
    @Since( "2.3.0" )
  7. def precisionByLabel: Array[Double]

    Returns precision for each label (category).

    Returns precision for each label (category).

    Annotations
    @Since( "2.3.0" )
  8. def recallByLabel: Array[Double]

    Returns recall for each label (category).

    Returns recall for each label (category).

    Annotations
    @Since( "2.3.0" )
  9. def truePositiveRateByLabel: Array[Double]

    Returns true positive rate for each label (category).

    Returns true positive rate for each label (category).

    Annotations
    @Since( "2.3.0" )
  10. def weightedFMeasure: Double

    Returns weighted averaged f1-measure.

    Returns weighted averaged f1-measure.

    Annotations
    @Since( "2.3.0" )
  11. def weightedFMeasure(beta: Double): Double

    Returns weighted averaged f-measure.

    Returns weighted averaged f-measure.

    Annotations
    @Since( "2.3.0" )
  12. def weightedFalsePositiveRate: Double

    Returns weighted false positive rate.

    Returns weighted false positive rate.

    Annotations
    @Since( "2.3.0" )
  13. def weightedPrecision: Double

    Returns weighted averaged precision.

    Returns weighted averaged precision.

    Annotations
    @Since( "2.3.0" )
  14. def weightedRecall: Double

    Returns weighted averaged recall.

    Returns weighted averaged recall. (equals to precision, recall and f-measure)

    Annotations
    @Since( "2.3.0" )
  15. def weightedTruePositiveRate: Double

    Returns weighted true positive rate.

    Returns weighted true positive rate. (equals to precision, recall and f-measure)

    Annotations
    @Since( "2.3.0" )