Packages

class LogisticRegression extends ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel] with LogisticRegressionParams with DefaultParamsWritable with Logging

Logistic regression. Supports:

  • Multinomial logistic (softmax) regression.
  • Binomial logistic regression.

This class supports fitting traditional logistic regression model by LBFGS/OWLQN and bound (box) constrained logistic regression model by LBFGSB.

Since 3.1.0, it supports stacking instances into blocks and using GEMV/GEMM for better performance. The block size will be 1.0 MB, if param maxBlockSizeInMB is set 0.0 by default.

Annotations
@Since( "1.2.0" )
Source
LogisticRegression.scala
Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. LogisticRegression
  2. DefaultParamsWritable
  3. MLWritable
  4. LogisticRegressionParams
  5. HasMaxBlockSizeInMB
  6. HasAggregationDepth
  7. HasThreshold
  8. HasWeightCol
  9. HasStandardization
  10. HasTol
  11. HasFitIntercept
  12. HasMaxIter
  13. HasElasticNetParam
  14. HasRegParam
  15. ProbabilisticClassifier
  16. ProbabilisticClassifierParams
  17. HasThresholds
  18. HasProbabilityCol
  19. Classifier
  20. ClassifierParams
  21. HasRawPredictionCol
  22. Predictor
  23. PredictorParams
  24. HasPredictionCol
  25. HasFeaturesCol
  26. HasLabelCol
  27. Estimator
  28. PipelineStage
  29. Logging
  30. Params
  31. Serializable
  32. Serializable
  33. Identifiable
  34. AnyRef
  35. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new LogisticRegression()
    Annotations
    @Since( "1.4.0" )
  2. new LogisticRegression(uid: String)
    Annotations
    @Since( "1.2.0" )

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
    LogisticRegressionPredictorEstimatorPipelineStageParams
    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
    PredictorEstimator
  29. def fit(dataset: Dataset[_], paramMaps: Seq[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 getMaxBlockSizeInMB: Double

    Definition Classes
    HasMaxBlockSizeInMB
  45. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  46. 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

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

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  49. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  50. final def getProbabilityCol: String

    Definition Classes
    HasProbabilityCol
  51. final def getRawPredictionCol: String

    Definition Classes
    HasRawPredictionCol
  52. final def getRegParam: Double

    Definition Classes
    HasRegParam
  53. final def getStandardization: Boolean

    Definition Classes
    HasStandardization
  54. 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.

  55. 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" )
  56. final def getTol: Double

    Definition Classes
    HasTol
  57. def getUpperBoundsOnCoefficients: Matrix

    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )
  58. def getUpperBoundsOnIntercepts: Vector

    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )
  59. final def getWeightCol: String

    Definition Classes
    HasWeightCol
  60. 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
  61. 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
  62. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  63. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  64. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. 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
  66. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  67. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  68. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  69. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  70. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  71. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  78. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. 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" )
  83. 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" )
  84. 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
  85. final val maxIter: IntParam

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

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

    Definition Classes
    HasMaxIter
  86. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  87. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  88. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  89. 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.

  90. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  91. 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
  92. 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
  93. final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes
    HasRegParam
  94. 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( ... )
  95. 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
  96. 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
  97. 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
  98. 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" )
  99. 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
  100. 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
  101. 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" )
  102. 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" )
  103. def setFeaturesCol(value: String): LogisticRegression

    Definition Classes
    Predictor
  104. 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" )
  105. def setLabelCol(value: String): LogisticRegression

    Definition Classes
    Predictor
  106. 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" )
  107. 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" )
  108. def setMaxBlockSizeInMB(value: Double): LogisticRegression.this.type

    Sets the value of param maxBlockSizeInMB.

    Sets the value of param maxBlockSizeInMB. Default is 0.0, then 1.0 MB will be chosen.

    Annotations
    @Since( "3.1.0" )
  109. 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" )
  110. def setPredictionCol(value: String): LogisticRegression

    Definition Classes
    Predictor
  111. def setProbabilityCol(value: String): LogisticRegression

    Definition Classes
    ProbabilisticClassifier
  112. def setRawPredictionCol(value: String): LogisticRegression

    Definition Classes
    Classifier
  113. def setRegParam(value: Double): LogisticRegression.this.type

    Set the regularization parameter.

    Set the regularization parameter. Default is 0.0.

    Annotations
    @Since( "1.2.0" )
  114. 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" )
  115. 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" )
  116. 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" )
  117. 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" )
  118. 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" )
  119. 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" )
  120. 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" )
  121. 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
  122. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  123. 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
  124. 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
  125. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  126. 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
  127. 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
    LogisticRegressionPredictor
  128. 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
    PredictorPipelineStage
  129. 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()
  130. 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
    LogisticRegressionIdentifiable
    Annotations
    @Since( "1.4.0" )
  131. 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" )
  132. 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" )
  133. def usingBoundConstrainedOptimization: Boolean
    Attributes
    protected
    Definition Classes
    LogisticRegressionParams
  134. 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
  135. 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
  136. 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
  137. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  138. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  139. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  140. 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
  141. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritableMLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from LogisticRegressionParams

Inherited from HasMaxBlockSizeInMB

Inherited from HasAggregationDepth

Inherited from HasThreshold

Inherited from HasWeightCol

Inherited from HasStandardization

Inherited from HasTol

Inherited from HasFitIntercept

Inherited from HasMaxIter

Inherited from HasElasticNetParam

Inherited from HasRegParam

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

Members

Parameter setters

Parameter getters

(expert-only) Parameters

A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

(expert-only) Parameter setters

(expert-only) Parameter getters