GeneralizedLinearRegressionModel

Companion object GeneralizedLinearRegressionModel

class GeneralizedLinearRegressionModel extends RegressionModel[Vector, GeneralizedLinearRegressionModel] with GeneralizedLinearRegressionBase with MLWritable with HasTrainingSummary[GeneralizedLinearRegressionTrainingSummary]

Model produced by GeneralizedLinearRegression.

Annotations: @Since( "2.0.0" )
Source: GeneralizedLinearRegression.scala

Linear Supertypes

HasTrainingSummary[GeneralizedLinearRegressionTrainingSummary], MLWritable, GeneralizedLinearRegressionBase, HasAggregationDepth, HasSolver, HasWeightCol, HasRegParam, HasTol, HasMaxIter, HasFitIntercept, RegressionModel[Vector, GeneralizedLinearRegressionModel], PredictionModel[Vector, GeneralizedLinearRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[GeneralizedLinearRegressionModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any

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Inherited

GeneralizedLinearRegressionModel
HasTrainingSummary
MLWritable
GeneralizedLinearRegressionBase
HasAggregationDepth
HasSolver
HasWeightCol
HasRegParam
HasTol
HasMaxIter
HasFitIntercept
RegressionModel
PredictionModel
PredictorParams
HasPredictionCol
HasFeaturesCol
HasLabelCol
Model
Transformer
PipelineStage
Logging
Params
Serializable
Serializable
Identifiable
AnyRef
Any

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Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def $[T](param: Param[T]): T
An alias for getOrDefault().
An alias for getOrDefault().

Attributes
protected
Definition Classes
Params
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final val aggregationDepth: IntParam
Param for suggested depth for treeAggregate (>= 2).
Param for suggested depth for treeAggregate (>= 2).

Definition Classes
HasAggregationDepth
final def asInstanceOf[T0]: T0

Definition Classes
Any
final def clear(param: Param[_]): GeneralizedLinearRegressionModel.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
val coefficients: Vector

Annotations
@Since( "2.0.0" )
def copy(extra: ParamMap): GeneralizedLinearRegressionModel
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
GeneralizedLinearRegressionModel → Model → Transformer → PipelineStage → Params
Annotations
@Since( "2.0.0" )
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
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
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def evaluate(dataset: Dataset[_]): GeneralizedLinearRegressionSummary
Evaluate the model on the given dataset, returning a summary of the results.
Evaluate the model on the given dataset, returning a summary of the results.

Annotations
@Since( "2.0.0" )
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
def explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See explainParam().

Definition Classes
Params
final def extractParamMap(): ParamMap
extractParamMap with no extra values.
extractParamMap with no extra values.

Definition Classes
Params
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
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" )
final val featuresCol: Param[String]
Param for features column name.
Param for features column name.

Definition Classes
HasFeaturesCol
def featuresDataType: DataType
Returns the SQL DataType corresponding to the FeaturesType type parameter.
Returns the SQL DataType corresponding to the FeaturesType type parameter.
This is used by validateAndTransformSchema(). This workaround is needed since SQL has different APIs for Scala and Java.
The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.

Attributes
protected
Definition Classes
PredictionModel
def finalize(): Unit

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final val fitIntercept: BooleanParam
Param for whether to fit an intercept term.
Param for whether to fit an intercept term.

Definition Classes
HasFitIntercept
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
final def getAggregationDepth: Int

Definition Classes
HasAggregationDepth
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@native()
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
def getFamily: String

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

Definition Classes
HasFeaturesCol
final def getFitIntercept: Boolean

Definition Classes
HasFitIntercept
final def getLabelCol: String

Definition Classes
HasLabelCol
def getLink: String

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

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

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

Definition Classes
HasMaxIter
def getOffsetCol: String

Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since( "2.3.0" )
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
def getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.

Definition Classes
Params
final def getPredictionCol: String

Definition Classes
HasPredictionCol
final def getRegParam: Double

Definition Classes
HasRegParam
final def getSolver: String

Definition Classes
HasSolver
final def getTol: Double

Definition Classes
HasTol
def getVariancePower: Double

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

Definition Classes
HasWeightCol
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
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
def hasParent: Boolean
Indicates whether this Model has a corresponding parent.
Indicates whether this Model has a corresponding parent.

Definition Classes
Model
def hasSummary: Boolean
Indicates whether a training summary exists for this model instance.
Indicates whether a training summary exists for this model instance.

Definition Classes
HasTrainingSummary
Annotations
@Since( "3.0.0" )
def hashCode(): Int

Definition Classes
AnyRef → Any
Annotations
@native()
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
val intercept: Double

Annotations
@Since( "2.0.0" )
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
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.

Definition Classes
Params
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
final val labelCol: Param[String]
Param for label column name.
Param for label column name.

Definition Classes
HasLabelCol
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" )
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" )
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" )
def log: Logger

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logName: String

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
final val maxIter: IntParam
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).

Definition Classes
HasMaxIter
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@native()
val numFeatures: Int
Returns the number of features the model was trained on.
Returns the number of features the model was trained on. If unknown, returns -1

Definition Classes
GeneralizedLinearRegressionModel → PredictionModel
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" )
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.
var parent: Estimator[GeneralizedLinearRegressionModel]
The parent estimator that produced this model.
The parent estimator that produced this model.

Definition Classes
Model
Note
For ensembles' component Models, this value can be null.
def predict(features: Vector): Double
Predict label for the given features.
Predict label for the given features. This method is used to implement transform() and output predictionCol.

Definition Classes
GeneralizedLinearRegressionModel → PredictionModel
final val predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.

Definition Classes
HasPredictionCol
final val regParam: DoubleParam
Param for regularization parameter (>= 0).
Param for regularization parameter (>= 0).

Definition Classes
HasRegParam
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( ... )
final def set(paramPair: ParamPair[_]): GeneralizedLinearRegressionModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): GeneralizedLinearRegressionModel.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
final def set[T](param: Param[T], value: T): GeneralizedLinearRegressionModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Definition Classes
Params
final def setDefault(paramPairs: ParamPair[_]*): GeneralizedLinearRegressionModel.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
final def setDefault[T](param: Param[T], value: T): GeneralizedLinearRegressionModel.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
def setFeaturesCol(value: String): GeneralizedLinearRegressionModel

Definition Classes
PredictionModel
def setLinkPredictionCol(value: String): GeneralizedLinearRegressionModel.this.type
Sets the link prediction (linear predictor) column name.
Sets the link prediction (linear predictor) column name.

Annotations
@Since( "2.0.0" )
def setParent(parent: Estimator[GeneralizedLinearRegressionModel]): GeneralizedLinearRegressionModel
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).

Definition Classes
Model
def setPredictionCol(value: String): GeneralizedLinearRegressionModel

Definition Classes
PredictionModel
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" )
def summary: GeneralizedLinearRegressionTrainingSummary
Gets R-like summary of model on training set.
Gets R-like summary of model on training set. An exception is thrown if there is no summary available.

Definition Classes
GeneralizedLinearRegressionModel → HasTrainingSummary
Annotations
@Since( "2.0.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
GeneralizedLinearRegressionModel → Identifiable → AnyRef → Any
Annotations
@Since( "3.0.0" )
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
def transform(dataset: Dataset[_]): DataFrame
Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.
Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.
dataset
input dataset
returns
transformed dataset with predictionCol of type Double

Definition Classes
GeneralizedLinearRegressionModel → PredictionModel → Transformer
def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
Transforms the dataset with provided parameter map as additional parameters.
Transforms the dataset with provided parameter map as additional parameters.
dataset
input dataset
paramMap
additional parameters, overwrite embedded params
returns
transformed dataset

Definition Classes
Transformer
Annotations
@Since( "2.0.0" )
def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
Transforms the dataset with optional parameters
Transforms the dataset with optional parameters
dataset
input dataset
firstParamPair
the first param pair, overwrite embedded params
otherParamPairs
other param pairs, overwrite embedded params
returns
transformed dataset

Definition Classes
Transformer
Annotations
@Since( "2.0.0" ) @varargs()
def transformImpl(dataset: Dataset[_]): DataFrame

Attributes
protected
Definition Classes
GeneralizedLinearRegressionModel → PredictionModel
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
PredictionModel → PipelineStage
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()
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
GeneralizedLinearRegressionModel → Identifiable
Annotations
@Since( "2.0.0" )
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" )
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" )
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
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
def write: MLWriter
Returns a org.apache.spark.ml.util.MLWriter instance for this ML instance.
Returns a org.apache.spark.ml.util.MLWriter instance for this ML instance.
For GeneralizedLinearRegressionModel, this does NOT currently save the training summary. An option to save summary may be added in the future.

Definition Classes
GeneralizedLinearRegressionModel → MLWritable
Annotations
@Since( "2.0.0" )

Inherited from HasTrainingSummary[GeneralizedLinearRegressionTrainingSummary]

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 RegressionModel[Vector, GeneralizedLinearRegressionModel]

Inherited from PredictionModel[Vector, GeneralizedLinearRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[GeneralizedLinearRegressionModel]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

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.

Packages

GeneralizedLinearRegressionModel

Companion object GeneralizedLinearRegressionModel

class GeneralizedLinearRegressionModel extends RegressionModel[Vector, GeneralizedLinearRegressionModel] with GeneralizedLinearRegressionBase with MLWritable with HasTrainingSummary[GeneralizedLinearRegressionTrainingSummary]

Value Members

Inherited from HasTrainingSummary[GeneralizedLinearRegressionTrainingSummary]

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 RegressionModel[Vector, GeneralizedLinearRegressionModel]

Inherited from PredictionModel[Vector, GeneralizedLinearRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[GeneralizedLinearRegressionModel]

Inherited from Transformer

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

Members

Parameter setters

Parameter getters

(expert-only) Parameters

(expert-only) Parameter getters

Packages

GeneralizedLinearRegressionModel 

Companion object GeneralizedLinearRegressionModel

class GeneralizedLinearRegressionModel extends RegressionModel[Vector, GeneralizedLinearRegressionModel] with GeneralizedLinearRegressionBase with MLWritable with HasTrainingSummary[GeneralizedLinearRegressionTrainingSummary]

Value Members

Inherited from HasTrainingSummary[GeneralizedLinearRegressionTrainingSummary]

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 RegressionModel[Vector, GeneralizedLinearRegressionModel]

Inherited from PredictionModel[Vector, GeneralizedLinearRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[GeneralizedLinearRegressionModel]

Inherited from Transformer

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

Members

Parameter setters

Parameter getters

(expert-only) Parameters

(expert-only) Parameter getters

GeneralizedLinearRegressionModel