LinearRegressionModel

Value Members

final def !=(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def !=(arg0: Any): Boolean

Definition Classes
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: AnyRef): Boolean

Definition Classes
AnyRef
final def ==(arg0: Any): Boolean

Definition Classes
Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
final def clear(param: Param[_]): LinearRegressionModel.this.type

Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.

Attributes
protected
Definition Classes
Params
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def copy(extra: ParamMap): LinearRegressionModel

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.

Definition Classes
LinearRegressionModel → Model → Transformer → PipelineStage → Params
See also
defaultCopy()
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.
to
the target instance
extra
extra params to be copied
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 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
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
Params
See also
explainParam()
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.
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 < user-supplied values < extra.

Definition Classes
Params
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[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
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 getClass(): Class[_]

Definition Classes
AnyRef → Any
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
final def getElasticNetParam: Double

Definition Classes
HasElasticNetParam
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol
final def getLabelCol: String

Definition Classes
HasLabelCol
final def getMaxIter: Int

Definition Classes
HasMaxIter
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 getTol: Double

Definition Classes
HasTol
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 hashCode(): Int

Definition Classes
AnyRef → Any
val intercept: Double
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
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
final def notifyAll(): Unit

Definition Classes
AnyRef
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.
Note: Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

Definition Classes
Params
var parent: Estimator[LinearRegressionModel]

The parent estimator that produced this model.
The parent estimator that produced this model. Note: For ensembles' component Models, this value can be null.

Definition Classes
Model
def predict(features: Vector): Double

Predict label for the given features.
Predict label for the given features. This internal method is used to implement transform() and output predictionCol.

Attributes
protected
Definition Classes
LinearRegressionModel → 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
final def set(paramPair: ParamPair[_]): LinearRegressionModel.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): LinearRegressionModel.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): LinearRegressionModel.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 setDefault(paramPairs: ParamPair[_]*): LinearRegressionModel.this.type

Sets default values for a list of params.
Sets default values for a list of params.
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
Annotations
@varargs()
final def setDefault[T](param: Param[T], value: T): LinearRegressionModel.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
def setFeaturesCol(value: String): LinearRegressionModel

Definition Classes
PredictionModel
def setParent(parent: Estimator[LinearRegressionModel]): LinearRegressionModel

Sets the parent of this model (Java API).
Sets the parent of this model (Java API).

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

Definition Classes
PredictionModel
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.
Param for the convergence tolerance for iterative algorithms.

Definition Classes
HasTol
def transform(dataset: DataFrame): 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
PredictionModel → Transformer
def transform(dataset: DataFrame, 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
def transform(dataset: DataFrame, 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
@varargs()
def transformSchema(schema: StructType): StructType

:: DeveloperApi ::
:: DeveloperApi ::
Derives the output schema from the input schema.

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
LinearRegressionModel → Identifiable
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., org.apache.spark.mllib.linalg.VectorUDT for vector features.
returns
output schema

Attributes
protected
Definition Classes
PredictorParams
def validateParams(): Unit

Validates parameter values stored internally.
Validates parameter values stored internally. Raise an exception if any parameter value is invalid.
This only needs to check for interactions between parameters. Parameter value checks which do not depend on other parameters are handled by Param.validate(). This method does not handle input/output column parameters; those are checked during schema validation.

Definition Classes
Params
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( ... )
val weights: Vector

Inherited from LinearRegressionParams

Inherited from HasTol

Inherited from HasMaxIter

Inherited from HasElasticNetParam

Inherited from HasRegParam

Inherited from RegressionModel[Vector, LinearRegressionModel]

Inherited from PredictionModel[Vector, LinearRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[LinearRegressionModel]

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

class LinearRegressionModel extends RegressionModel[Vector, LinearRegressionModel] with LinearRegressionParams

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def $[T](param: Param[T]): T

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

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

def clone(): AnyRef

def copy(extra: ParamMap): LinearRegressionModel

def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

final def defaultCopy[T <: Params](extra: ParamMap): T

final val elasticNetParam: DoubleParam

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def explainParam(param: Param[_]): String

def explainParams(): String

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

final val featuresCol: Param[String]

def featuresDataType: DataType

def finalize(): Unit

final def get[T](param: Param[T]): Option[T]

final def getClass(): Class[_]

final def getDefault[T](param: Param[T]): Option[T]

final def getElasticNetParam: Double

final def getFeaturesCol: String

final def getLabelCol: String

final def getMaxIter: Int

final def getOrDefault[T](param: Param[T]): T

def getParam(paramName: String): Param[Any]

final def getPredictionCol: String

final def getRegParam: Double

final def getTol: Double

final def hasDefault[T](param: Param[T]): Boolean

def hasParam(paramName: String): Boolean

def hasParent: Boolean

def hashCode(): Int

val intercept: Double

final def isDefined(param: Param[_]): Boolean

final def isInstanceOf[T0]: Boolean

final def isSet(param: Param[_]): Boolean

def isTraceEnabled(): Boolean

final val labelCol: Param[String]

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

final val maxIter: IntParam

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

lazy val params: Array[Param[_]]

var parent: Estimator[LinearRegressionModel]

def predict(features: Vector): Double

final val predictionCol: Param[String]

final val regParam: DoubleParam

final def set(paramPair: ParamPair[_]): LinearRegressionModel.this.type

final def set(param: String, value: Any): LinearRegressionModel.this.type

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

final def setDefault(paramPairs: ParamPair[_]*): LinearRegressionModel.this.type

final def setDefault[T](param: Param[T], value: T): LinearRegressionModel.this.type

def setFeaturesCol(value: String): LinearRegressionModel

def setParent(parent: Estimator[LinearRegressionModel]): LinearRegressionModel

def setPredictionCol(value: String): LinearRegressionModel

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

final val tol: DoubleParam

def transform(dataset: DataFrame): DataFrame