Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

2.  final def ##(): Int

   Definition Classes

   AnyRef → Any

3.  final def $[T](param: Param[T]): T

   An alias for getOrDefault().

   An alias for getOrDefault().

   Attributes

   protected

   Definition Classes

   Params

4.  final def ==(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

5.  final val aggregationDepth: IntParam

   Param for suggested depth for treeAggregate (>= 2).

   Param for suggested depth for treeAggregate (>= 2).

   Definition Classes

   HasAggregationDepth

6.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

7.  def binarySummary: BinaryLogisticRegressionTrainingSummary

   Gets summary of model on training set.

   Gets summary of model on training set. An exception is thrown if hasSummary is false or it is a multiclass model.

   Annotations

   @Since( "2.3.0" )

8.  def checkThresholdConsistency(): Unit

   If threshold and thresholds are both set, ensures they are consistent.

   If threshold and thresholds are both set, ensures they are consistent.

   Attributes

   protected

   Definition Classes

   LogisticRegressionParams

   Exceptions thrown

   IllegalArgumentException if threshold and thresholds are not equivalent

9.  final def clear(param: Param[_]): LogisticRegressionModel.this.type

   Clears the user-supplied value for the input param.

   Clears the user-supplied value for the input param.

   Definition Classes

   Params

10.  def clone(): AnyRef

    Attributes

    protected[lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( ... ) @native()

11.  val coefficientMatrix: Matrix

    Annotations

    @Since( "2.1.0" )

12.  def coefficients: Vector

    A vector of model coefficients for "binomial" logistic regression.

    A vector of model coefficients for "binomial" logistic regression. If this model was trained using the "multinomial" family then an exception is thrown.

    returns

    Vector

    Annotations

    @Since( "2.0.0" )

13.  def copy(extra: ParamMap): LogisticRegressionModel

    Creates a copy of this instance with the same UID and some extra params.

    Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

    Definition Classes

    LogisticRegressionModel → Model → Transformer → PipelineStage → Params

    Annotations

    @Since( "1.4.0" )

14.  def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

    Copies param values from this instance to another instance for params shared by them.

    Copies param values from this instance to another instance for params shared by them.

    This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

    to

    the target instance, which should work with the same set of default Params as this source instance

    extra

    extra params to be copied to the target's paramMap

    returns

    the target instance with param values copied

    Attributes

    protected

    Definition Classes

    Params

15.  final def defaultCopy[T <: Params](extra: ParamMap): T

    Default implementation of copy with extra params.

    Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

    Attributes

    protected

    Definition Classes

    Params

16.  final val elasticNetParam: DoubleParam

    Param for the ElasticNet mixing parameter, in range [0, 1].

    Param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.

    Definition Classes

    HasElasticNetParam

17.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

18.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

19.  def evaluate(dataset: Dataset[_]): LogisticRegressionSummary

    Evaluates the model on a test dataset.

    Evaluates the model on a test dataset.

    dataset

    Test dataset to evaluate model on.

    Annotations

    @Since( "2.0.0" )

20.  def explainParam(param: Param[_]): String

    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes

    Params

21.  def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes

    Params

22.  final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes

    Params

23.  final def extractParamMap(extra: ParamMap): ParamMap

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Definition Classes

    Params

24.  final val family: Param[String]

    Param for the name of family which is a description of the label distribution to be used in the model.

    Param for the name of family which is a description of the label distribution to be used in the model. Supported options:

    • "auto": Automatically select the family based on the number of classes: If numClasses == 1 || numClasses == 2, set to "binomial". Else, set to "multinomial"
    • "binomial": Binary logistic regression with pivoting.
    • "multinomial": Multinomial logistic (softmax) regression without pivoting. Default is "auto".

    Definition Classes

    LogisticRegressionParams

    Annotations

    @Since( "2.1.0" )

25.  final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes

    HasFeaturesCol

26.  def featuresDataType: DataType

    Returns the SQL DataType corresponding to the FeaturesType type parameter.

    Returns the SQL DataType corresponding to the FeaturesType type parameter.

    This is used by validateAndTransformSchema(). This workaround is needed since SQL has different APIs for Scala and Java.

    The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.

    Attributes

    protected

    Definition Classes

    PredictionModel

27.  def finalize(): Unit

    Attributes

    protected[lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

28.  final val fitIntercept: BooleanParam

    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

    Definition Classes

    HasFitIntercept

29.  final def get[T](param: Param[T]): Option[T]

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes

    Params

30.  final def getAggregationDepth: Int
    Definition Classes

    HasAggregationDepth

31.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

32.  final def getDefault[T](param: Param[T]): Option[T]

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes

    Params

33.  final def getElasticNetParam: Double
    Definition Classes

    HasElasticNetParam

34.  def getFamily: String
    Definition Classes

    LogisticRegressionParams

    Annotations

    @Since( "2.1.0" )

35.  final def getFeaturesCol: String
    Definition Classes

    HasFeaturesCol

36.  final def getFitIntercept: Boolean
    Definition Classes

    HasFitIntercept

37.  final def getLabelCol: String
    Definition Classes

    HasLabelCol

38.  def getLowerBoundsOnCoefficients: Matrix
    Definition Classes

    LogisticRegressionParams

    Annotations

    @Since( "2.2.0" )

39.  def getLowerBoundsOnIntercepts: Vector
    Definition Classes

    LogisticRegressionParams

    Annotations

    @Since( "2.2.0" )

40.  final def getMaxBlockSizeInMB: Double
    Definition Classes

    HasMaxBlockSizeInMB

41.  final def getMaxIter: Int
    Definition Classes

    HasMaxIter

42.  final def getOrDefault[T](param: Param[T]): T

    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes

    Params

43.  def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes

    Params

44.  final def getPredictionCol: String
    Definition Classes

    HasPredictionCol

45.  final def getProbabilityCol: String
    Definition Classes

    HasProbabilityCol

46.  final def getRawPredictionCol: String
    Definition Classes

    HasRawPredictionCol

47.  final def getRegParam: Double
    Definition Classes

    HasRegParam

48.  final def getStandardization: Boolean
    Definition Classes

    HasStandardization

49.  def getThreshold: Double

    Get threshold for binary classification.

    Get threshold for binary classification.

    If thresholds is set with length 2 (i.e., binary classification), this returns the equivalent threshold:

    1 / (1 + thresholds(0) / thresholds(1))

    . Otherwise, returns threshold if set, or its default value if unset.

    1 / (1 + thresholds(0) / thresholds(1)) }}} Otherwise, returns threshold if set, or its default value if unset.

    Definition Classes

    LogisticRegressionModel → LogisticRegressionParams → HasThreshold

    Annotations

    @Since( "1.5.0" )

    Exceptions thrown

    IllegalArgumentException if thresholds is set to an array of length other than 2.

50.  def getThresholds: Array[Double]

    Get thresholds for binary or multiclass classification.

    Get thresholds for binary or multiclass classification.

    If thresholds is set, return its value. Otherwise, if threshold is set, return the equivalent thresholds for binary classification: (1-threshold, threshold). If neither are set, throw an exception.

    Definition Classes

    LogisticRegressionModel → LogisticRegressionParams → HasThresholds

    Annotations

    @Since( "1.5.0" )

51.  final def getTol: Double
    Definition Classes

    HasTol

52.  def getUpperBoundsOnCoefficients: Matrix
    Definition Classes

    LogisticRegressionParams

    Annotations

    @Since( "2.2.0" )

53.  def getUpperBoundsOnIntercepts: Vector
    Definition Classes

    LogisticRegressionParams

    Annotations

    @Since( "2.2.0" )

54.  final def getWeightCol: String
    Definition Classes

    HasWeightCol

55.  final def hasDefault[T](param: Param[T]): Boolean

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes

    Params

56.  def hasParam(paramName: String): Boolean

    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes

    Params

57.  def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes

    Model

58.  def hasSummary: Boolean

    Indicates whether a training summary exists for this model instance.

    Indicates whether a training summary exists for this model instance.

    Definition Classes

    HasTrainingSummary

    Annotations

    @Since( "3.0.0" )

59.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

60.  def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

    Attributes

    protected

    Definition Classes

    Logging

61.  def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    Attributes

    protected

    Definition Classes

    Logging

62.  def intercept: Double

    The model intercept for "binomial" logistic regression.

    The model intercept for "binomial" logistic regression. If this model was fit with the "multinomial" family then an exception is thrown.

    returns

    Double

    Annotations

    @Since( "1.3.0" )

63.  val interceptVector: Vector

    Annotations

    @Since( "2.1.0" )

64.  final def isDefined(param: Param[_]): Boolean

    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes

    Params

65.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

66.  final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes

    Params

67.  def isTraceEnabled(): Boolean

    Attributes

    protected

    Definition Classes

    Logging

68.  final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes

    HasLabelCol

69.  def log: Logger

    Attributes

    protected

    Definition Classes

    Logging

70.  def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

71.  def logDebug(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

72.  def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

73.  def logError(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

74.  def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

75.  def logInfo(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

76.  def logName: String

    Attributes

    protected

    Definition Classes

    Logging

77.  def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

78.  def logTrace(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

79.  def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

80.  def logWarning(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

81.  val lowerBoundsOnCoefficients: Param[Matrix]

    The lower bounds on coefficients if fitting under bound constrained optimization.

    The lower bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. Otherwise, it throws exception. Default is none.

    Definition Classes

    LogisticRegressionParams

    Annotations

    @Since( "2.2.0" )

82.  val lowerBoundsOnIntercepts: Param[Vector]

    The lower bounds on intercepts if fitting under bound constrained optimization.

    The lower bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must be equal to 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.

    Definition Classes

    LogisticRegressionParams

    Annotations

    @Since( "2.2.0" )

83.  final val maxBlockSizeInMB: DoubleParam

    Param for Maximum memory in MB for stacking input data into blocks.

    Param for Maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0..

    Definition Classes

    HasMaxBlockSizeInMB

84.  final val maxIter: IntParam

    Param for maximum number of iterations (>= 0).

    Param for maximum number of iterations (>= 0).

    Definition Classes

    HasMaxIter

85.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

86.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

87.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

88.  val numClasses: Int

    Number of classes (values which the label can take).

    Number of classes (values which the label can take).

    Definition Classes

    LogisticRegressionModel → ClassificationModel

    Annotations

    @Since( "1.3.0" )

89.  val numFeatures: Int

    Returns the number of features the model was trained on.

    Returns the number of features the model was trained on. If unknown, returns -1

    Definition Classes

    LogisticRegressionModel → PredictionModel

    Annotations

    @Since( "1.6.0" )

90.  lazy val params: Array[Param[_]]

    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes

    Params

    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

91.  var parent: Estimator[LogisticRegressionModel]

    The parent estimator that produced this model.

    The parent estimator that produced this model.

    Definition Classes

    Model

    Note

    For ensembles' component Models, this value can be null.

92.  def predict(features: Vector): Double

    Predict label for the given feature vector.

    Predict label for the given feature vector. The behavior of this can be adjusted using thresholds.

    Definition Classes

    LogisticRegressionModel → ClassificationModel → PredictionModel

93.  def predictProbability(features: Vector): Vector

    Predict the probability of each class given the features.

    Predict the probability of each class given the features. These predictions are also called class conditional probabilities.

    This internal method is used to implement transform() and output probabilityCol.

    returns

    Estimated class conditional probabilities

    Definition Classes

    ProbabilisticClassificationModel

    Annotations

    @Since( "3.0.0" )

94.  def predictRaw(features: Vector): Vector

    Raw prediction for each possible label.

    Raw prediction for each possible label. The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives a measure of confidence in each possible label (where larger = more confident). This internal method is used to implement transform() and output rawPredictionCol.

    returns

    vector where element i is the raw prediction for label i. This raw prediction may be any real number, where a larger value indicates greater confidence for that label.

    Definition Classes

    LogisticRegressionModel → ClassificationModel

    Annotations

    @Since( "3.0.0" )

95.  final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes

    HasPredictionCol

96.  def probability2prediction(probability: Vector): Double

    Given a vector of class conditional probabilities, select the predicted label.

    Given a vector of class conditional probabilities, select the predicted label. This supports thresholds which favor particular labels.

    returns

    predicted label

    Attributes

    protected

    Definition Classes

    LogisticRegressionModel → ProbabilisticClassificationModel

97.  final val probabilityCol: Param[String]

    Param for Column name for predicted class conditional probabilities.

    Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

    Definition Classes

    HasProbabilityCol

98.  def raw2prediction(rawPrediction: Vector): Double

    Given a vector of raw predictions, select the predicted label.

    Given a vector of raw predictions, select the predicted label. This may be overridden to support thresholds which favor particular labels.

    returns

    predicted label

    Attributes

    protected

    Definition Classes

    LogisticRegressionModel → ProbabilisticClassificationModel → ClassificationModel

99.  def raw2probability(rawPrediction: Vector): Vector

    Non-in-place version of raw2probabilityInPlace()

    Non-in-place version of raw2probabilityInPlace()

    Attributes

    protected

    Definition Classes

    ProbabilisticClassificationModel

100.  def raw2probabilityInPlace(rawPrediction: Vector): Vector

     Estimate the probability of each class given the raw prediction, doing the computation in-place.

     Estimate the probability of each class given the raw prediction, doing the computation in-place. These predictions are also called class conditional probabilities.

     This internal method is used to implement transform() and output probabilityCol.

     returns

     Estimated class conditional probabilities (modified input vector)

     Attributes

     protected

     Definition Classes

     LogisticRegressionModel → ProbabilisticClassificationModel

101.  final val rawPredictionCol: Param[String]

     Param for raw prediction (a.k.a.

     Param for raw prediction (a.k.a. confidence) column name.

     Definition Classes

     HasRawPredictionCol

102.  final val regParam: DoubleParam

     Param for regularization parameter (>= 0).

     Param for regularization parameter (>= 0).

     Definition Classes

     HasRegParam

103.  def save(path: String): Unit

     Saves this ML instance to the input path, a shortcut of write.save(path).

     Saves this ML instance to the input path, a shortcut of write.save(path).

     Definition Classes

     MLWritable

     Annotations

     @Since( "1.6.0" ) @throws( ... )

104.  final def set(paramPair: ParamPair[_]): LogisticRegressionModel.this.type

     Sets a parameter in the embedded param map.

     Sets a parameter in the embedded param map.

     Attributes

     protected

     Definition Classes

     Params

105.  final def set(param: String, value: Any): LogisticRegressionModel.this.type

     Sets a parameter (by name) in the embedded param map.

     Sets a parameter (by name) in the embedded param map.

     Attributes

     protected

     Definition Classes

     Params

106.  final def set[T](param: Param[T], value: T): LogisticRegressionModel.this.type

     Sets a parameter in the embedded param map.

     Sets a parameter in the embedded param map.

     Definition Classes

     Params

107.  final def setDefault(paramPairs: ParamPair[_]*): LogisticRegressionModel.this.type

     Sets default values for a list of params.

     Sets default values for a list of params.

     Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

     paramPairs

     a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

     Attributes

     protected

     Definition Classes

     Params

108.  final def setDefault[T](param: Param[T], value: T): LogisticRegressionModel.this.type

     Sets a default value for a param.

     Sets a default value for a param.

     param

     param to set the default value. Make sure that this param is initialized before this method gets called.

     value

     the default value

     Attributes

     protected

     Definition Classes

     Params

109.  def setFeaturesCol(value: String): LogisticRegressionModel
     Definition Classes

     PredictionModel

110.  def setParent(parent: Estimator[LogisticRegressionModel]): LogisticRegressionModel

     Sets the parent of this model (Java API).

     Sets the parent of this model (Java API).

     Definition Classes

     Model

111.  def setPredictionCol(value: String): LogisticRegressionModel
     Definition Classes

     PredictionModel

112.  def setProbabilityCol(value: String): LogisticRegressionModel
     Definition Classes

     ProbabilisticClassificationModel

113.  def setRawPredictionCol(value: String): LogisticRegressionModel
     Definition Classes

     ClassificationModel

114.  def setThreshold(value: Double): LogisticRegressionModel.this.type

     Set threshold in binary classification, in range [0, 1].

     Set threshold in binary classification, in range [0, 1].

     If the estimated probability of class label 1 is greater than threshold, then predict 1, else 0. A high threshold encourages the model to predict 0 more often; a low threshold encourages the model to predict 1 more often.

     Note: Calling this with threshold p is equivalent to calling setThresholds(Array(1-p, p)). When setThreshold() is called, any user-set value for thresholds will be cleared. If both threshold and thresholds are set in a ParamMap, then they must be equivalent.

     Default is 0.5.

     Definition Classes

     LogisticRegressionModel → LogisticRegressionParams

     Annotations

     @Since( "1.5.0" )

115.  def setThresholds(value: Array[Double]): LogisticRegressionModel.this.type

     Set thresholds in multiclass (or binary) classification to adjust the probability of predicting each class.

     Set thresholds in multiclass (or binary) classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values greater than 0, excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

     Note: When setThresholds() is called, any user-set value for threshold will be cleared. If both threshold and thresholds are set in a ParamMap, then they must be equivalent.

     Definition Classes

     LogisticRegressionModel → LogisticRegressionParams → ProbabilisticClassificationModel

     Annotations

     @Since( "1.5.0" )

116.  final val standardization: BooleanParam

     Param for whether to standardize the training features before fitting the model.

     Param for whether to standardize the training features before fitting the model.

     Definition Classes

     HasStandardization

117.  def summary: LogisticRegressionTrainingSummary

     Gets summary of model on training set.

     Gets summary of model on training set. An exception is thrown if hasSummary is false.

     Definition Classes

     LogisticRegressionModel → HasTrainingSummary

     Annotations

     @Since( "1.5.0" )

118.  final def synchronized[T0](arg0: ⇒ T0): T0

     Definition Classes

     AnyRef

119.  val threshold: DoubleParam

     Param for threshold in binary classification prediction, in range [0, 1].

     Param for threshold in binary classification prediction, in range [0, 1].

     Definition Classes

     HasThreshold

120.  val thresholds: DoubleArrayParam

     Param for Thresholds in multi-class classification to adjust the probability of predicting each class.

     Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

     Definition Classes

     HasThresholds

121.  def toString(): String

     Definition Classes

     LogisticRegressionModel → Identifiable → AnyRef → Any

122.  final val tol: DoubleParam

     Param for the convergence tolerance for iterative algorithms (>= 0).

     Param for the convergence tolerance for iterative algorithms (>= 0).

     Definition Classes

     HasTol

123.  def transform(dataset: Dataset[_]): DataFrame

     Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:

     Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:

     • predicted labels as predictionCol of type Double
     • raw predictions (confidences) as rawPredictionCol of type Vector
     • probability of each class as probabilityCol of type Vector.

     dataset

     input dataset

     returns

     transformed dataset

     Definition Classes

     ProbabilisticClassificationModel → ClassificationModel → PredictionModel → Transformer

124.  def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

     Transforms the dataset with provided parameter map as additional parameters.

     Transforms the dataset with provided parameter map as additional parameters.

     dataset

     input dataset

     paramMap

     additional parameters, overwrite embedded params

     returns

     transformed dataset

     Definition Classes

     Transformer

     Annotations

     @Since( "2.0.0" )

125.  def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

     Transforms the dataset with optional parameters

     Transforms the dataset with optional parameters

     dataset

     input dataset

     firstParamPair

     the first param pair, overwrite embedded params

     otherParamPairs

     other param pairs, overwrite embedded params

     returns

     transformed dataset

     Definition Classes

     Transformer

     Annotations

     @Since( "2.0.0" ) @varargs()

126.  final def transformImpl(dataset: Dataset[_]): DataFrame

     Definition Classes

     ClassificationModel → PredictionModel

127.  def transformSchema(schema: StructType): StructType

     Check transform validity and derive the output schema from the input schema.

     Check transform validity and derive the output schema from the input schema.

     We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

     Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

     Definition Classes

     ProbabilisticClassificationModel → ClassificationModel → PredictionModel → PipelineStage

128.  def transformSchema(schema: StructType, logging: Boolean): StructType

     :: DeveloperApi ::

     :: DeveloperApi ::

     Derives the output schema from the input schema and parameters, optionally with logging.

     This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

     Attributes

     protected

     Definition Classes

     PipelineStage

     Annotations

     @DeveloperApi()

129.  val uid: String

     An immutable unique ID for the object and its derivatives.

     An immutable unique ID for the object and its derivatives.

     Definition Classes

     LogisticRegressionModel → Identifiable

     Annotations

     @Since( "1.4.0" )

130.  val upperBoundsOnCoefficients: Param[Matrix]

     The upper bounds on coefficients if fitting under bound constrained optimization.

     The upper bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. Otherwise, it throws exception. Default is none.

     Definition Classes

     LogisticRegressionParams

     Annotations

     @Since( "2.2.0" )

131.  val upperBoundsOnIntercepts: Param[Vector]

     The upper bounds on intercepts if fitting under bound constrained optimization.

     The upper bounds on intercepts if fitting under bound constrained optimization. The bound vector size must be equal to 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.

     Definition Classes

     LogisticRegressionParams

     Annotations

     @Since( "2.2.0" )

132.  def usingBoundConstrainedOptimization: Boolean

     Attributes

     protected

     Definition Classes

     LogisticRegressionParams

133.  def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

     Validates and transforms the input schema with the provided param map.

     Validates and transforms the input schema with the provided param map.

     schema

     input schema

     fitting

     whether this is in fitting

     featuresDataType

     SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

     returns

     output schema

     Attributes

     protected

     Definition Classes

     LogisticRegressionParams → ProbabilisticClassifierParams → ClassifierParams → PredictorParams

134.  final def wait(): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

135.  final def wait(arg0: Long, arg1: Int): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

136.  final def wait(arg0: Long): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... ) @native()

137.  final val weightCol: Param[String]

     Param for weight column name.

     Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

     Definition Classes

     HasWeightCol

138.  def write: MLWriter

     Returns a org.apache.spark.ml.util.MLWriter instance for this ML instance.

     Returns a org.apache.spark.ml.util.MLWriter instance for this ML instance.

     For LogisticRegressionModel, this does NOT currently save the training summary. An option to save summary may be added in the future.

     This also does not save the parent currently.

     Definition Classes

     LogisticRegressionModel → MLWritable

     Annotations

     @Since( "1.6.0" )