org.apache.spark.ml.regression
GeneralizedLinearRegression
Companion object GeneralizedLinearRegression
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
- Grouped
- Alphabetic
- By Inheritance
- GeneralizedLinearRegression
- DefaultParamsWritable
- MLWritable
- GeneralizedLinearRegressionBase
- HasAggregationDepth
- HasSolver
- HasWeightCol
- HasRegParam
- HasTol
- HasMaxIter
- HasFitIntercept
- Regressor
- Predictor
- PredictorParams
- HasPredictionCol
- HasFeaturesCol
- HasLabelCol
- Estimator
- PipelineStage
- Logging
- Params
- Serializable
- Identifiable
- AnyRef
- Any
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- Public
- Protected
Instance Constructors
Type Members
- implicit class LogStringContext extends AnyRef
- Definition Classes
- Logging
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[_]): GeneralizedLinearRegression.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(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
- 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")
- 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 toparamMap. 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: AnyRef): Boolean
- Definition Classes
- AnyRef → Any
- 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
extractParamMapwith no extra values.extractParamMapwith 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 fit(dataset: Dataset[_]): GeneralizedLinearRegressionModel
Fits a model to the input data.
- 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")
- 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")
- 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()
- 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[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @IntrinsicCandidate() @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 hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @IntrinsicCandidate() @native()
- def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
- Attributes
- protected
- Definition Classes
- Logging
- def initializeLogIfNecessary(isInterpreter: Boolean): Unit
- Attributes
- protected
- Definition Classes
- Logging
- 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(entry: LogEntry, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logDebug(entry: LogEntry): 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(entry: LogEntry, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logError(entry: LogEntry): 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(entry: LogEntry, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logInfo(entry: LogEntry): 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(entry: LogEntry, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logTrace(entry: LogEntry): 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(entry: LogEntry, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logWarning(entry: LogEntry): 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
- @IntrinsicCandidate() @native()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @IntrinsicCandidate() @native()
- 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.
- 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("If the input path already exists but overwrite is not enabled.")
- 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
- 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
- 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
- def setAggregationDepth(value: Int): GeneralizedLinearRegression.this.type
- Annotations
- @Since("3.0.0")
- 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
- final def setDefault[T](param: Param[T], value: T): GeneralizedLinearRegression.this.type
Sets a default value for a param.
- 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")
- def setFeaturesCol(value: String): GeneralizedLinearRegression
- Definition Classes
- Predictor
- 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")
- def setLabelCol(value: String): GeneralizedLinearRegression
- Definition Classes
- Predictor
- 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")
- 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")
- 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")
- 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")
- 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")
- def setPredictionCol(value: String): GeneralizedLinearRegression
- Definition Classes
- Predictor
- 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")
- 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")
- 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")
- 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")
- 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")
- 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")
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- def toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
- 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 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
- 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
transformSchemaand raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled byParam.validate().Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
- Definition Classes
- Predictor → 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
- GeneralizedLinearRegression → 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.,
VectorUDTfor 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(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- 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 withLogContext(context: HashMap[String, String])(body: => Unit): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def write: MLWriter
Returns an
MLWriterinstance for this ML instance.Returns an
MLWriterinstance for this ML instance.- Definition Classes
- DefaultParamsWritable → MLWritable
Deprecated Value Members
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated
(Since version 9)
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 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.