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 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

8.  final def clear(param: Param[_]): LogisticRegression.this.type

   Clears the user-supplied value for the input param.

   Clears the user-supplied value for the input param.

   Definition Classes

   Params

9.  def clone(): AnyRef

   Attributes

   protected[lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... ) @native()

10.  def copy(extra: ParamMap): LogisticRegression

    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

    LogisticRegression → Predictor → Estimator → PipelineStage → Params

    Annotations

    @Since( "1.4.0" )

11.  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

12.  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

13.  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

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

    Definition Classes

    AnyRef

15.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

16.  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

17.  def explainParams(): String

    Explains all params of this instance.

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

    Definition Classes

    Params

18.  def extractInstances(dataset: Dataset[_], numClasses: Int): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validates the label on the classifier is a valid integer in the range [0, numClasses).

    Attributes

    protected

    Definition Classes

    ClassifierParams

19.  def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validate the output instances with the given function.

    Attributes

    protected

    Definition Classes

    PredictorParams

20.  def extractInstances(dataset: Dataset[_]): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes

    protected

    Definition Classes

    PredictorParams

21.  def extractLabeledPoints(dataset: Dataset[_], numClasses: Int): RDD[LabeledPoint]

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    dataset

    DataFrame with columns for labels (org.apache.spark.sql.types.NumericType) and features (Vector).

    numClasses

    Number of classes label can take. Labels must be integers in the range [0, numClasses).

    Attributes

    protected

    Definition Classes

    Classifier

    Note

    Throws SparkException if any label is a non-integer or is negative

22.  def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes

    protected

    Definition Classes

    Predictor

23.  final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes

    Params

24.  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

25.  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" )

26.  final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes

    HasFeaturesCol

27.  def finalize(): Unit

    Attributes

    protected[lang]

    Definition Classes

    AnyRef

    Annotations

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

28.  def fit(dataset: Dataset[_]): LogisticRegressionModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes

    Predictor → Estimator

29.  def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[LogisticRegressionModel]

    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted models, matching the input parameter maps

    Definition Classes

    Estimator

    Annotations

    @Since( "2.0.0" )

30.  def fit(dataset: Dataset[_], paramMap: ParamMap): LogisticRegressionModel

    Fits a single model to the input data with provided parameter map.

    Fits a single model to the input data with provided parameter map.

    dataset

    input dataset

    paramMap

    Parameter map. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes

    Estimator

    Annotations

    @Since( "2.0.0" )

31.  def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): LogisticRegressionModel

    Fits a single model to the input data with optional parameters.

    Fits a single model to the input data with optional parameters.

    dataset

    input dataset

    firstParamPair

    the first param pair, overrides embedded params

    otherParamPairs

    other param pairs. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes

    Estimator

    Annotations

    @Since( "2.0.0" ) @varargs()

32.  final val fitIntercept: BooleanParam

    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

    Definition Classes

    HasFitIntercept

33.  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

34.  final def getAggregationDepth: Int
    Definition Classes

    HasAggregationDepth

35.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

36.  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

37.  final def getElasticNetParam: Double
    Definition Classes

    HasElasticNetParam

38.  def getFamily: String
    Definition Classes

    LogisticRegressionParams

    Annotations

    @Since( "2.1.0" )

39.  final def getFeaturesCol: String
    Definition Classes

    HasFeaturesCol

40.  final def getFitIntercept: Boolean
    Definition Classes

    HasFitIntercept

41.  final def getLabelCol: String
    Definition Classes

    HasLabelCol

42.  def getLowerBoundsOnCoefficients: Matrix
    Definition Classes

    LogisticRegressionParams

    Annotations

    @Since( "2.2.0" )

43.  def getLowerBoundsOnIntercepts: Vector
    Definition Classes

    LogisticRegressionParams

    Annotations

    @Since( "2.2.0" )

44.  final def getMaxIter: Int
    Definition Classes

    HasMaxIter

45.  def getNumClasses(dataset: Dataset[_], maxNumClasses: Int = 100): Int

    Get the number of classes.

    Get the number of classes. This looks in column metadata first, and if that is missing, then this assumes classes are indexed 0,1,...,numClasses-1 and computes numClasses by finding the maximum label value.

    Label validation (ensuring all labels are integers >= 0) needs to be handled elsewhere, such as in extractLabeledPoints().

    dataset

    Dataset which contains a column labelCol

    maxNumClasses

    Maximum number of classes allowed when inferred from data. If numClasses is specified in the metadata, then maxNumClasses is ignored.

    returns

    number of classes

    Attributes

    protected

    Definition Classes

    Classifier

    Exceptions thrown

    IllegalArgumentException if metadata does not specify numClasses, and the actual numClasses exceeds maxNumClasses

46.  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

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

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes

    Params

48.  final def getPredictionCol: String
    Definition Classes

    HasPredictionCol

49.  final def getProbabilityCol: String
    Definition Classes

    HasProbabilityCol

50.  final def getRawPredictionCol: String
    Definition Classes

    HasRawPredictionCol

51.  final def getRegParam: Double
    Definition Classes

    HasRegParam

52.  final def getStandardization: Boolean
    Definition Classes

    HasStandardization

53.  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

    LogisticRegression → LogisticRegressionParams → HasThreshold

    Annotations

    @Since( "1.5.0" )

    Exceptions thrown

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

54.  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

    LogisticRegression → LogisticRegressionParams → HasThresholds

    Annotations

    @Since( "1.5.0" )

55.  final def getTol: Double
    Definition Classes

    HasTol

56.  def getUpperBoundsOnCoefficients: Matrix
    Definition Classes

    LogisticRegressionParams

    Annotations

    @Since( "2.2.0" )

57.  def getUpperBoundsOnIntercepts: Vector
    Definition Classes

    LogisticRegressionParams

    Annotations

    @Since( "2.2.0" )

58.  final def getWeightCol: String
    Definition Classes

    HasWeightCol

59.  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

60.  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

61.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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 maxIter: IntParam

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

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

    Definition Classes

    HasMaxIter

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

    Definition Classes

    AnyRef

85.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

86.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

87.  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.

88.  final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes

    HasPredictionCol

89.  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

90.  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

91.  final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes

    HasRegParam

92.  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( ... )

93.  final def set(paramPair: ParamPair[_]): LogisticRegression.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes

    protected

    Definition Classes

    Params

94.  final def set(param: String, value: Any): LogisticRegression.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

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

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes

    Params

96.  def setAggregationDepth(value: Int): LogisticRegression.this.type

    Suggested depth for treeAggregate (greater than or equal to 2).

    Suggested depth for treeAggregate (greater than or equal to 2). If the dimensions of features or the number of partitions are large, this param could be adjusted to a larger size. Default is 2.

    Annotations

    @Since( "2.1.0" )

97.  final def setDefault(paramPairs: ParamPair[_]*): LogisticRegression.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

98.  final def setDefault[T](param: Param[T], value: T): LogisticRegression.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

99.  def setElasticNetParam(value: Double): LogisticRegression.this.type

    Set the ElasticNet mixing parameter.

    Set the ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. For alpha in (0,1), the penalty is a combination of L1 and L2. Default is 0.0 which is an L2 penalty.

    Note: Fitting under bound constrained optimization only supports L2 regularization, so throws exception if this param is non-zero value.

    Annotations

    @Since( "1.4.0" )

100.  def setFamily(value: String): LogisticRegression.this.type

     Sets the value of param family.

     Sets the value of param family. Default is "auto".

     Annotations

     @Since( "2.1.0" )

101.  def setFeaturesCol(value: String): LogisticRegression
     Definition Classes

     Predictor

102.  def setFitIntercept(value: Boolean): LogisticRegression.this.type

     Whether to fit an intercept term.

     Whether to fit an intercept term. Default is true.

     Annotations

     @Since( "1.4.0" )

103.  def setLabelCol(value: String): LogisticRegression
     Definition Classes

     Predictor

104.  def setLowerBoundsOnCoefficients(value: Matrix): LogisticRegression.this.type

     Set the lower bounds on coefficients if fitting under bound constrained optimization.

     Set the lower bounds on coefficients if fitting under bound constrained optimization.

     Annotations

     @Since( "2.2.0" )

105.  def setLowerBoundsOnIntercepts(value: Vector): LogisticRegression.this.type

     Set the lower bounds on intercepts if fitting under bound constrained optimization.

     Set the lower bounds on intercepts if fitting under bound constrained optimization.

     Annotations

     @Since( "2.2.0" )

106.  def setMaxIter(value: Int): LogisticRegression.this.type

     Set the maximum number of iterations.

     Set the maximum number of iterations. Default is 100.

     Annotations

     @Since( "1.2.0" )

107.  def setPredictionCol(value: String): LogisticRegression
     Definition Classes

     Predictor

108.  def setProbabilityCol(value: String): LogisticRegression
     Definition Classes

     ProbabilisticClassifier

109.  def setRawPredictionCol(value: String): LogisticRegression
     Definition Classes

     Classifier

110.  def setRegParam(value: Double): LogisticRegression.this.type

     Set the regularization parameter.

     Set the regularization parameter. Default is 0.0.

     Annotations

     @Since( "1.2.0" )

111.  def setStandardization(value: Boolean): LogisticRegression.this.type

     Whether to standardize the training features before fitting the model.

     Whether to standardize the training features before fitting the model. The coefficients of models will be always returned on the original scale, so it will be transparent for users. Note that with/without standardization, the models should be always converged to the same solution when no regularization is applied. In R's GLMNET package, the default behavior is true as well. Default is true.

     Annotations

     @Since( "1.5.0" )

112.  def setThreshold(value: Double): LogisticRegression.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

     LogisticRegression → LogisticRegressionParams

     Annotations

     @Since( "1.5.0" )

113.  def setThresholds(value: Array[Double]): LogisticRegression.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

     LogisticRegression → LogisticRegressionParams → ProbabilisticClassifier

     Annotations

     @Since( "1.5.0" )

114.  def setTol(value: Double): LogisticRegression.this.type

     Set the convergence tolerance of iterations.

     Set the convergence tolerance of iterations. Smaller value will lead to higher accuracy at the cost of more iterations. Default is 1E-6.

     Annotations

     @Since( "1.4.0" )

115.  def setUpperBoundsOnCoefficients(value: Matrix): LogisticRegression.this.type

     Set the upper bounds on coefficients if fitting under bound constrained optimization.

     Set the upper bounds on coefficients if fitting under bound constrained optimization.

     Annotations

     @Since( "2.2.0" )

116.  def setUpperBoundsOnIntercepts(value: Vector): LogisticRegression.this.type

     Set the upper bounds on intercepts if fitting under bound constrained optimization.

     Set the upper bounds on intercepts if fitting under bound constrained optimization.

     Annotations

     @Since( "2.2.0" )

117.  def setWeightCol(value: String): LogisticRegression.this.type

     Sets the value of param weightCol.

     Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one.

     Annotations

     @Since( "1.6.0" )

118.  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

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

     Definition Classes

     AnyRef

120.  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

121.  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

122.  def toString(): String

     Definition Classes

     Identifiable → AnyRef → Any

123.  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

124.  def train(dataset: Dataset[_], handlePersistence: Boolean): LogisticRegressionModel

     Attributes

     protected[spark]

125.  def train(dataset: Dataset[_]): LogisticRegressionModel

     Train a model using the given dataset and parameters.

     Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.

     dataset

     Training dataset

     returns

     Fitted model

     Attributes

     protected[spark]

     Definition Classes

     LogisticRegression → Predictor

126.  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

     Predictor → PipelineStage

127.  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()

128.  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

     LogisticRegression → Identifiable

     Annotations

     @Since( "1.4.0" )

129.  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" )

130.  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" )

131.  def usingBoundConstrainedOptimization: Boolean

     Attributes

     protected

     Definition Classes

     LogisticRegressionParams

132.  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

133.  def validateLabel(label: Double, numClasses: Int): Unit

     Validates the label on the classifier is a valid integer in the range [0, numClasses).

     Validates the label on the classifier is a valid integer in the range [0, numClasses).

     label

     The label to validate.

     numClasses

     Number of classes label can take. Labels must be integers in the range [0, numClasses).

     Attributes

     protected

     Definition Classes

     Classifier

134.  def validateNumClasses(numClasses: Int): Unit

     Validates that number of classes is greater than zero.

     Validates that number of classes is greater than zero.

     numClasses

     Number of classes label can take.

     Attributes

     protected

     Definition Classes

     Classifier

135.  final def wait(): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

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

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

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

     Definition Classes

     AnyRef

     Annotations

     @throws( ... ) @native()

138.  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

139.  def write: MLWriter

     Returns an MLWriter instance for this ML instance.

     Returns an MLWriter instance for this ML instance.

     Definition Classes

     DefaultParamsWritable → MLWritable