Packages

class GeneralizedLinearRegression extends Regressor[Vector, GeneralizedLinearRegression, GeneralizedLinearRegressionModel] with GeneralizedLinearRegressionBase with DefaultParamsWritable with Logging

Fit a Generalized Linear Model (see Generalized linear model (Wikipedia)) specified by giving a symbolic description of the linear predictor (link function) and a description of the error distribution (family). It supports "gaussian", "binomial", "poisson", "gamma" and "tweedie" as family. Valid link functions for each family is listed below. The first link function of each family is the default one.

  • "gaussian" : "identity", "log", "inverse"
  • "binomial" : "logit", "probit", "cloglog"
  • "poisson" : "log", "identity", "sqrt"
  • "gamma" : "inverse", "identity", "log"
  • "tweedie" : power link function specified through "linkPower". The default link power in the tweedie family is 1 - variancePower.
Annotations
@Since( "2.0.0" )
Source
GeneralizedLinearRegression.scala
Linear Supertypes
DefaultParamsWritable, MLWritable, GeneralizedLinearRegressionBase, HasAggregationDepth, HasSolver, HasWeightCol, HasRegParam, HasTol, HasMaxIter, HasFitIntercept, Regressor[Vector, GeneralizedLinearRegression, GeneralizedLinearRegressionModel], Predictor[Vector, GeneralizedLinearRegression, GeneralizedLinearRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[GeneralizedLinearRegressionModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. GeneralizedLinearRegression
  2. DefaultParamsWritable
  3. MLWritable
  4. GeneralizedLinearRegressionBase
  5. HasAggregationDepth
  6. HasSolver
  7. HasWeightCol
  8. HasRegParam
  9. HasTol
  10. HasMaxIter
  11. HasFitIntercept
  12. Regressor
  13. Predictor
  14. PredictorParams
  15. HasPredictionCol
  16. HasFeaturesCol
  17. HasLabelCol
  18. Estimator
  19. PipelineStage
  20. Logging
  21. Params
  22. Serializable
  23. Serializable
  24. Identifiable
  25. AnyRef
  26. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new GeneralizedLinearRegression()
    Annotations
    @Since( "2.0.0" )
  2. new GeneralizedLinearRegression(uid: String)
    Annotations
    @Since( "2.0.0" )

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. final def clear(param: Param[_]): GeneralizedLinearRegression.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  8. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native() @IntrinsicCandidate()
  9. def copy(extra: ParamMap): GeneralizedLinearRegression

    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
    GeneralizedLinearRegression → Predictor → Estimator → PipelineStage → Params
    Annotations
    @Since( "2.0.0" )
  10. 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
  11. 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
  12. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  14. 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
  15. def explainParams(): String

    Explains all params of this instance.

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

    Definition Classes
    Params
  16. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  17. 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
  18. final val family: Param[String]

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

    Param for the name of family which is a description of the error distribution to be used in the model. Supported options: "gaussian", "binomial", "poisson", "gamma" and "tweedie". Default is "gaussian".

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  19. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  20. def fit(dataset: Dataset[_]): GeneralizedLinearRegressionModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    Predictor → Estimator
  21. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[GeneralizedLinearRegressionModel]

    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" )
  22. def fit(dataset: Dataset[_], paramMap: ParamMap): GeneralizedLinearRegressionModel

    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" )
  23. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): GeneralizedLinearRegressionModel

    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()
  24. final val fitIntercept: BooleanParam

    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

    Definition Classes
    HasFitIntercept
  25. 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
  26. final def getAggregationDepth: Int

    Definition Classes
    HasAggregationDepth
  27. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @IntrinsicCandidate()
  28. 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
  29. def getFamily: String

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  30. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  31. final def getFitIntercept: Boolean

    Definition Classes
    HasFitIntercept
  32. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  33. def getLink: String

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  34. def getLinkPower: Double

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.2.0" )
  35. def getLinkPredictionCol: String

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  36. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  37. def getOffsetCol: String

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.3.0" )
  38. 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
  39. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  40. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  41. final def getRegParam: Double

    Definition Classes
    HasRegParam
  42. final def getSolver: String

    Definition Classes
    HasSolver
  43. final def getTol: Double

    Definition Classes
    HasTol
  44. def getVariancePower: Double

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.2.0" )
  45. final def getWeightCol: String

    Definition Classes
    HasWeightCol
  46. 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
  47. 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
  48. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @IntrinsicCandidate()
  49. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  50. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  51. 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
  52. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  53. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

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

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  56. final val link: Param[String]

    Param for the name of link function which provides the relationship between the linear predictor and the mean of the distribution function.

    Param for the name of link function which provides the relationship between the linear predictor and the mean of the distribution function. Supported options: "identity", "log", "inverse", "logit", "probit", "cloglog" and "sqrt". This is used only when family is not "tweedie". The link function for the "tweedie" family must be specified through linkPower.

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  57. final val linkPower: DoubleParam

    Param for the index in the power link function.

    Param for the index in the power link function. Only applicable to the Tweedie family. Note that link power 0, 1, -1 or 0.5 corresponds to the Log, Identity, Inverse or Sqrt link, respectively. When not set, this value defaults to 1 - variancePower, which matches the R "statmod" package.

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.2.0" )
  58. final val linkPredictionCol: Param[String]

    Param for link prediction (linear predictor) column name.

    Param for link prediction (linear predictor) column name. Default is not set, which means we do not output link prediction.

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  59. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  60. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  67. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. final val maxIter: IntParam

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

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

    Definition Classes
    HasMaxIter
  72. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  73. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @IntrinsicCandidate()
  74. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @IntrinsicCandidate()
  75. final val offsetCol: Param[String]

    Param for offset column name.

    Param for offset column name. If this is not set or empty, we treat all instance offsets as 0.0. The feature specified as offset has a constant coefficient of 1.0.

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.3.0" )
  76. 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.

  77. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  78. final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes
    HasRegParam
  79. 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( ... )
  80. final def set(paramPair: ParamPair[_]): GeneralizedLinearRegression.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  81. final def set(param: String, value: Any): GeneralizedLinearRegression.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
  82. final def set[T](param: Param[T], value: T): GeneralizedLinearRegression.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  83. def setAggregationDepth(value: Int): GeneralizedLinearRegression.this.type

    Annotations
    @Since( "3.0.0" )
  84. final def setDefault(paramPairs: ParamPair[_]*): GeneralizedLinearRegression.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
  85. final def setDefault[T](param: Param[T], value: T): GeneralizedLinearRegression.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[ml]
    Definition Classes
    Params
  86. def setFamily(value: String): GeneralizedLinearRegression.this.type

    Sets the value of param family.

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

    Annotations
    @Since( "2.0.0" )
  87. def setFeaturesCol(value: String): GeneralizedLinearRegression

    Definition Classes
    Predictor
  88. def setFitIntercept(value: Boolean): GeneralizedLinearRegression.this.type

    Sets if we should fit the intercept.

    Sets if we should fit the intercept. Default is true.

    Annotations
    @Since( "2.0.0" )
  89. def setLabelCol(value: String): GeneralizedLinearRegression

    Definition Classes
    Predictor
  90. def setLink(value: String): GeneralizedLinearRegression.this.type

    Sets the value of param link.

    Sets the value of param link. Used only when family is not "tweedie".

    Annotations
    @Since( "2.0.0" )
  91. def setLinkPower(value: Double): GeneralizedLinearRegression.this.type

    Sets the value of param linkPower.

    Sets the value of param linkPower. Used only when family is "tweedie".

    Annotations
    @Since( "2.2.0" )
  92. def setLinkPredictionCol(value: String): GeneralizedLinearRegression.this.type

    Sets the link prediction (linear predictor) column name.

    Sets the link prediction (linear predictor) column name.

    Annotations
    @Since( "2.0.0" )
  93. def setMaxIter(value: Int): GeneralizedLinearRegression.this.type

    Sets the maximum number of iterations (applicable for solver "irls").

    Sets the maximum number of iterations (applicable for solver "irls"). Default is 25.

    Annotations
    @Since( "2.0.0" )
  94. def setOffsetCol(value: String): GeneralizedLinearRegression.this.type

    Sets the value of param offsetCol.

    Sets the value of param offsetCol. If this is not set or empty, we treat all instance offsets as 0.0. Default is not set, so all instances have offset 0.0.

    Annotations
    @Since( "2.3.0" )
  95. def setPredictionCol(value: String): GeneralizedLinearRegression

    Definition Classes
    Predictor
  96. def setRegParam(value: Double): GeneralizedLinearRegression.this.type

    Sets the regularization parameter for L2 regularization.

    Sets the regularization parameter for L2 regularization. The regularization term is

    $$ 0.5 * regParam * L2norm(coefficients)^2 $$
    Default is 0.0.

    Annotations
    @Since( "2.0.0" )
  97. def setSolver(value: String): GeneralizedLinearRegression.this.type

    Sets the solver algorithm used for optimization.

    Sets the solver algorithm used for optimization. Currently only supports "irls" which is also the default solver.

    Annotations
    @Since( "2.0.0" )
  98. def setTol(value: Double): GeneralizedLinearRegression.this.type

    Sets the convergence tolerance of iterations.

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

    Annotations
    @Since( "2.0.0" )
  99. def setVariancePower(value: Double): GeneralizedLinearRegression.this.type

    Sets the value of param variancePower.

    Sets the value of param variancePower. Used only when family is "tweedie". Default is 0.0, which corresponds to the "gaussian" family.

    Annotations
    @Since( "2.2.0" )
  100. def setWeightCol(value: String): GeneralizedLinearRegression.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. In the Binomial family, weights correspond to number of trials and should be integer. Non-integer weights are rounded to integer in AIC calculation.

    Annotations
    @Since( "2.0.0" )
  101. final val solver: Param[String]

    The solver algorithm for optimization.

    The solver algorithm for optimization. Supported options: "irls" (iteratively reweighted least squares). Default: "irls"

    Definition Classes
    GeneralizedLinearRegressionBase → HasSolver
    Annotations
    @Since( "2.0.0" )
  102. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  103. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  104. 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
  105. def train(dataset: Dataset[_]): GeneralizedLinearRegressionModel

    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
    Definition Classes
    GeneralizedLinearRegression → Predictor
  106. 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
  107. 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()
  108. 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
    GeneralizedLinearRegression → Identifiable
    Annotations
    @Since( "2.0.0" )
  109. 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

    Definition Classes
    GeneralizedLinearRegressionBase → PredictorParams
    Annotations
    @Since( "2.0.0" )
  110. final val variancePower: DoubleParam

    Param for the power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution.

    Param for the power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution. Only applicable to the Tweedie family. (see Tweedie Distribution (Wikipedia)) Supported values: 0 and [1, Inf). Note that variance power 0, 1, or 2 corresponds to the Gaussian, Poisson or Gamma family, respectively.

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.2.0" )
  111. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  112. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  113. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  114. 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
  115. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritable → MLWritable

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from GeneralizedLinearRegressionBase

Inherited from HasAggregationDepth

Inherited from HasSolver

Inherited from HasWeightCol

Inherited from HasRegParam

Inherited from HasTol

Inherited from HasMaxIter

Inherited from HasFitIntercept

Inherited from Regressor[Vector, GeneralizedLinearRegression, GeneralizedLinearRegressionModel]

Inherited from Predictor[Vector, GeneralizedLinearRegression, GeneralizedLinearRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Estimator[GeneralizedLinearRegressionModel]

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