LinearRegression

Instance Constructors

new LinearRegression()
new LinearRegression(uid: String)

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

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

Definition Classes
AnyRef → Any
final val aggregationDepth: IntParam

Definition Classes
HasAggregationDepth
final def asInstanceOf[T0]: T0

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

Definition Classes
Params
def clone(): AnyRef

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

Definition Classes
LinearRegression → SummarizableEstimator → Estimator → PipelineStage → Params
def copyValues[T <: Params](to: T, extra: ParamMap): T

Attributes
protected
Definition Classes
Params
final def defaultCopy[T <: Params](extra: ParamMap): T

Attributes
protected
Definition Classes
Params
final val elasticNetParam: DoubleParam

Definition Classes
HasElasticNetParam
final val epsilon: DoubleParam

Definition Classes
LinearRegressionParams
Annotations
@Since( "2.3.0" )
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
def explainParam(param: Param[_]): String

Definition Classes
Params
def explainParams(): String

Definition Classes
Params
final def extractParamMap(): ParamMap

Definition Classes
Params
final def extractParamMap(extra: ParamMap): ParamMap

Definition Classes
Params
def extractWeights(model: LinearRegressionModel, sparkSession: SparkSession, attributes: AttributeGroup): DataFrame
final val featuresCol: Param[String]

Definition Classes
HasFeaturesCol
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def fit(dataset: Dataset[_]): odkl.LinearRegressionModel

Definition Classes
LinearRegression → Estimator
def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[odkl.LinearRegressionModel]

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], paramMap: ParamMap): odkl.LinearRegressionModel

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): odkl.LinearRegressionModel

Definition Classes
Estimator
Annotations
@Since( "2.0.0" ) @varargs()
final val fitIntercept: BooleanParam

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

Definition Classes
Params
final def getAggregationDepth: Int

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

Definition Classes
AnyRef → Any
final def getDefault[T](param: Param[T]): Option[T]

Definition Classes
Params
final def getElasticNetParam: Double

Definition Classes
HasElasticNetParam
def getEpsilon: Double

Definition Classes
LinearRegressionParams
Annotations
@Since( "2.3.0" )
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol
final def getFitIntercept: Boolean

Definition Classes
HasFitIntercept
final def getLabelCol: String

Definition Classes
HasLabelCol
final def getLoss: String

Definition Classes
HasLoss
final def getMaxIter: Int

Definition Classes
HasMaxIter
final def getOrDefault[T](param: Param[T]): T

Definition Classes
Params
def getParam(paramName: String): Param[Any]

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 getStandardization: Boolean

Definition Classes
HasStandardization
final def getTol: Double

Definition Classes
HasTol
final def getWeightCol: String

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

Definition Classes
Params
def hasParam(paramName: String): Boolean

Definition Classes
Params
def hashCode(): Int

Definition Classes
AnyRef → Any
val index: String

Definition Classes
HasWeights
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

Definition Classes
Params
final def isInstanceOf[T0]: Boolean

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

Definition Classes
Params
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
final val labelCol: Param[String]

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 loss: Param[String]

Definition Classes
LinearRegressionParams → HasLoss
Annotations
@Since( "2.3.0" )
final val maxIter: IntParam

Definition Classes
HasMaxIter
val name: String

Definition Classes
HasWeights
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[_]]

Definition Classes
Params
final val predictionCol: Param[String]

Definition Classes
HasPredictionCol
final val regParam: DoubleParam

Definition Classes
HasRegParam
def save(path: String): Unit

Definition Classes
MLWritable
Annotations
@Since( "1.6.0" ) @throws( ... )
final def set(paramPair: ParamPair[_]): LinearRegression.this.type

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): LinearRegression.this.type

Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): LinearRegression.this.type

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

Suggested depth for treeAggregate (greater than or equal to 2).
Suggested depth for treeAggregate (greater than or equal to 2). If the dimensions of features or the number of partitions are large, this param could be adjusted to a larger size. Default is 2.

Annotations
@Since( "2.1.0" )
final def setDefault(paramPairs: ParamPair[_]*): LinearRegression.this.type

Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): LinearRegression.this.type

Attributes
protected
Definition Classes
Params
def setElasticNetParam(value: Double): LinearRegression.this.type

Set the ElasticNet mixing parameter.
Set the ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. For alpha in (0,1), the penalty is a combination of L1 and L2. Default is 0.0 which is an L2 penalty.
Note: Fitting with huber loss only supports None and L2 regularization, so throws exception if this param is non-zero value.

Annotations
@Since( "1.4.0" )
def setEpsilon(value: Double): LinearRegression.this.type

Sets the value of param epsilon.
Sets the value of param epsilon. Default is 1.35.

Annotations
@Since( "2.3.0" )
def setFeaturesCol(value: String): LinearRegression.this.type
def setFitIntercept(value: Boolean): LinearRegression.this.type

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

Annotations
@Since( "1.5.0" )
def setLabelCol(value: String): LinearRegression.this.type
def setLoss(value: String): LinearRegression.this.type

Sets the value of param loss.
Sets the value of param loss. Default is "squaredError".

Annotations
@Since( "2.3.0" )
def setMaxIter(value: Int): LinearRegression.this.type

Set the maximum number of iterations.
Set the maximum number of iterations. Default is 100.

Annotations
@Since( "1.3.0" )
def setPredictionCol(value: String): LinearRegression.this.type
def setRegParam(value: Double): LinearRegression.this.type

Set the regularization parameter.
Set the regularization parameter. Default is 0.0.

Annotations
@Since( "1.3.0" )
def setSolver(value: String): LinearRegression.this.type

Set the solver algorithm used for optimization.
Set the solver algorithm used for optimization. In case of linear regression, this can be "l-bfgs", "normal" and "auto".
- "l-bfgs" denotes Limited-memory BFGS which is a limited-memory quasi-Newton optimization method.
- "normal" denotes using Normal Equation as an analytical solution to the linear regression problem. This solver is limited to LinearRegression.MAX_FEATURES_FOR_NORMAL_SOLVER.
- "auto" (default) means that the solver algorithm is selected automatically. The Normal Equations solver will be used when possible, but this will automatically fall back to iterative optimization methods when needed.
Note: Fitting with huber loss doesn't support normal solver, so throws exception if this param was set with "normal".
Annotations
@Since( "1.6.0" )
def setStandardization(value: Boolean): LinearRegression.this.type

Whether to standardize the training features before fitting the model.
Whether to standardize the training features before fitting the model. The coefficients of models will be always returned on the original scale, so it will be transparent for users. Default is true.

Annotations
@Since( "1.5.0" )
Note
With/without standardization, the models should be always converged to the same solution when no regularization is applied. In R's GLMNET package, the default behavior is true as well.
def setTol(value: Double): LinearRegression.this.type

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

Annotations
@Since( "1.4.0" )
def setWeightCol(value: String): LinearRegression.this.type

Whether to over-/under-sample training instances according to the given weights in weightCol.
Whether to over-/under-sample training instances according to the given weights in weightCol. If not set or empty, all instances are treated equally (weight 1.0). Default is not set, so all instances have weight one.

Annotations
@Since( "1.6.0" )
final val solver: Param[String]

Definition Classes
LinearRegressionParams → HasSolver
Annotations
@Since( "1.6.0" )
final val standardization: BooleanParam

Definition Classes
HasStandardization
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
final val tol: DoubleParam

Definition Classes
HasTol
def transformSchema(schema: StructType): StructType

Definition Classes
LinearRegression → PipelineStage
def transformSchema(schema: StructType, logging: Boolean): StructType

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
val uid: String

Definition Classes
LinearRegression → Identifiable
def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

Attributes
protected
Definition Classes
LinearRegressionParams → PredictorParams
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 weight: String

Definition Classes
HasWeights
final val weightCol: Param[String]

Definition Classes
HasWeightCol
val weights: Block

Definition Classes
HasWeights
def write: MLWriter

Definition Classes
DefaultParamsWritable → MLWritable

Related Doc: package odkl

class LinearRegression extends Estimator[odkl.LinearRegressionModel] with SummarizableEstimator[odkl.LinearRegressionModel] with LinearRegressionParams with DefaultParamsWritable with HasWeights

Instance Constructors

new LinearRegression()

new LinearRegression(uid: String)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

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

final def ==(arg0: Any): Boolean

final val aggregationDepth: IntParam

final def asInstanceOf[T0]: T0

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

def clone(): AnyRef

def copy(extra: ParamMap): SummarizableEstimator[odkl.LinearRegressionModel]

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

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

final val elasticNetParam: DoubleParam

final val epsilon: 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

def extractWeights(model: LinearRegressionModel, sparkSession: SparkSession, attributes: AttributeGroup): DataFrame

final val featuresCol: Param[String]

def finalize(): Unit

def fit(dataset: Dataset[_]): odkl.LinearRegressionModel

def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[odkl.LinearRegressionModel]

def fit(dataset: Dataset[_], paramMap: ParamMap): odkl.LinearRegressionModel

def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): odkl.LinearRegressionModel

final val fitIntercept: BooleanParam

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

final def getAggregationDepth: Int

final def getClass(): Class[_]

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

final def getElasticNetParam: Double

def getEpsilon: Double

final def getFeaturesCol: String

final def getFitIntercept: Boolean

final def getLabelCol: String

final def getLoss: 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 getSolver: String

final def getStandardization: Boolean

final def getTol: Double

final def getWeightCol: String

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

def hasParam(paramName: String): Boolean

def hashCode(): Int

val index: String

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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 loss: Param[String]

final val maxIter: IntParam

val name: String

final def ne(arg0: AnyRef): Boolean