Class/Object

org.apache.spark.ml.regression

LinearRegression

Related Docs: object LinearRegression | package regression

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class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegressionModel] with LinearRegressionParams with DefaultParamsWritable with Logging

Linear regression.

The learning objective is to minimize the specified loss function, with regularization. This supports two kinds of loss:

This supports multiple types of regularization:

The squared error objective function is:

$$ \begin{align} \min_{w}\frac{1}{2n}{\sum_{i=1}^n(X_{i}w - y_{i})^{2} + \lambda\left[\frac{1-\alpha}{2}{||w||_{2}}^{2} + \alpha{||w||_{1}}\right]} \end{align} $$

The huber objective function is:

$$ \begin{align} \min_{w, \sigma}\frac{1}{2n}{\sum_{i=1}^n\left(\sigma + H_m\left(\frac{X_{i}w - y_{i}}{\sigma}\right)\sigma\right) + \frac{1}{2}\lambda {||w||_2}^2} \end{align} $$

where

$$ \begin{align} H_m(z) = \begin{cases} z^2, & \text {if } |z| < \epsilon, \\ 2\epsilon|z| - \epsilon^2, & \text{otherwise} \end{cases} \end{align} $$

Note: Fitting with huber loss only supports none and L2 regularization.

Annotations
@Since( "1.3.0" )
Source
LinearRegression.scala
Linear Supertypes
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Inherited
  1. LinearRegression
  2. DefaultParamsWritable
  3. MLWritable
  4. LinearRegressionParams
  5. HasLoss
  6. HasAggregationDepth
  7. HasSolver
  8. HasWeightCol
  9. HasStandardization
  10. HasFitIntercept
  11. HasTol
  12. HasMaxIter
  13. HasElasticNetParam
  14. HasRegParam
  15. Regressor
  16. Predictor
  17. PredictorParams
  18. HasPredictionCol
  19. HasFeaturesCol
  20. HasLabelCol
  21. Estimator
  22. PipelineStage
  23. Logging
  24. Params
  25. Serializable
  26. Serializable
  27. Identifiable
  28. AnyRef
  29. Any
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Visibility
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Instance Constructors

  1. new LinearRegression()

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    Annotations
    @Since( "1.4.0" )
  2. new LinearRegression(uid: String)

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    Annotations
    @Since( "1.3.0" )

Value Members

  1. final def !=(arg0: Any): Boolean

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

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    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

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    An alias for getOrDefault().

    An alias for getOrDefault().

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

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    Definition Classes
    AnyRef → Any
  5. final val aggregationDepth: IntParam

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    Param for suggested depth for treeAggregate (>= 2).

    Param for suggested depth for treeAggregate (>= 2).

    Definition Classes
    HasAggregationDepth
  6. final def asInstanceOf[T0]: T0

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

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    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

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

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    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
    LinearRegressionPredictorEstimatorPipelineStageParams
    Annotations
    @Since( "1.4.0" )
  10. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

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    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

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    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 val elasticNetParam: DoubleParam

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    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
  13. final val epsilon: DoubleParam

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    The shape parameter to control the amount of robustness.

    The shape parameter to control the amount of robustness. Must be > 1.0. At larger values of epsilon, the huber criterion becomes more similar to least squares regression; for small values of epsilon, the criterion is more similar to L1 regression. Default is 1.35 to get as much robustness as possible while retaining 95% statistical efficiency for normally distributed data. It matches sklearn HuberRegressor and is "M" from A robust hybrid of lasso and ridge regression. Only valid when "loss" is "huber".

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

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

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

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    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
  17. def explainParams(): String

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    Explains all params of this instance.

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

    Definition Classes
    Params
  18. def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]

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    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes
    protected
    Definition Classes
    Predictor
  19. final def extractParamMap(): ParamMap

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    extractParamMap with no extra values.

    extractParamMap with no extra values.

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

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    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
  21. final val featuresCol: Param[String]

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    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  22. def finalize(): Unit

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

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    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    PredictorEstimator
  24. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[LinearRegressionModel]

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

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

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    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()
  27. final val fitIntercept: BooleanParam

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    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

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

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    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  29. final def getAggregationDepth: Int

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

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

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    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  32. final def getElasticNetParam: Double

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    Definition Classes
    HasElasticNetParam
  33. def getEpsilon: Double

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

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    Definition Classes
    HasFeaturesCol
  35. final def getFitIntercept: Boolean

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    Definition Classes
    HasFitIntercept
  36. final def getLabelCol: String

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    Definition Classes
    HasLabelCol
  37. final def getLoss: String

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    Definition Classes
    HasLoss
  38. final def getMaxIter: Int

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

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

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    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  41. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol
  42. final def getRegParam: Double

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    Definition Classes
    HasRegParam
  43. final def getSolver: String

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    Definition Classes
    HasSolver
  44. final def getStandardization: Boolean

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    Definition Classes
    HasStandardization
  45. final def getTol: Double

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    Definition Classes
    HasTol
  46. final def getWeightCol: String

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

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    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

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

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    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
  49. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  50. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

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

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    protected
    Definition Classes
    Logging
  52. final def isDefined(param: Param[_]): Boolean

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    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
  53. final def isInstanceOf[T0]: Boolean

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

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    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  55. def isTraceEnabled(): Boolean

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

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    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  57. def log: Logger

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

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    protected
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    Logging
  59. def logDebug(msg: ⇒ String): Unit

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

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

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

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

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    protected
    Definition Classes
    Logging
  64. def logName: String

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

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

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

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

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    protected
    Definition Classes
    Logging
  69. final val loss: Param[String]

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    The loss function to be optimized.

    The loss function to be optimized. Supported options: "squaredError" and "huber". Default: "squaredError"

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

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

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

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

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    Definition Classes
    AnyRef
  72. final def notify(): Unit

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    Definition Classes
    AnyRef
  73. final def notifyAll(): Unit

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    AnyRef
  74. lazy val params: Array[Param[_]]

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    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.

  75. final val predictionCol: Param[String]

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    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  76. final val regParam: DoubleParam

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    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

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

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

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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

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

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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

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    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" )
  82. final def setDefault(paramPairs: ParamPair[_]*): LinearRegression.this.type

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

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    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
  84. def setElasticNetParam(value: Double): LinearRegression.this.type

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    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" )
  85. def setEpsilon(value: Double): LinearRegression.this.type

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    Sets the value of param epsilon.

    Sets the value of param epsilon. Default is 1.35.

    Annotations
    @Since( "2.3.0" )
  86. def setFeaturesCol(value: String): LinearRegression

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    Definition Classes
    Predictor
  87. def setFitIntercept(value: Boolean): LinearRegression.this.type

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    Set if we should fit the intercept.

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

    Annotations
    @Since( "1.5.0" )
  88. def setLabelCol(value: String): LinearRegression

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    Definition Classes
    Predictor
  89. def setLoss(value: String): LinearRegression.this.type

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    Sets the value of param loss.

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

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

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    Set the maximum number of iterations.

    Set the maximum number of iterations. Default is 100.

    Annotations
    @Since( "1.3.0" )
  91. def setPredictionCol(value: String): LinearRegression

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    Definition Classes
    Predictor
  92. def setRegParam(value: Double): LinearRegression.this.type

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    Set the regularization parameter.

    Set the regularization parameter. Default is 0.0.

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

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    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" )
  94. def setStandardization(value: Boolean): LinearRegression.this.type

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    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.

  95. def setTol(value: Double): LinearRegression.this.type

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    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" )
  96. def setWeightCol(value: String): LinearRegression.this.type

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    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" )
  97. final val solver: Param[String]

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    The solver algorithm for optimization.

    The solver algorithm for optimization. Supported options: "l-bfgs", "normal" and "auto". Default: "auto"

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

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    Param for whether to standardize the training features before fitting the model.

    Param for whether to standardize the training features before fitting the model.

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

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    Definition Classes
    AnyRef
  100. def toString(): String

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

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    Param for the convergence tolerance for iterative algorithms (>= 0).

    Param for the convergence tolerance for iterative algorithms (>= 0).

    Definition Classes
    HasTol
  102. def train(dataset: Dataset[_]): LinearRegressionModel

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    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
    LinearRegressionPredictor
  103. def transformSchema(schema: StructType): StructType

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    :: DeveloperApi ::

    :: DeveloperApi ::

    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
    PredictorPipelineStage
  104. def transformSchema(schema: StructType, logging: Boolean): StructType

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    :: 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()
  105. val uid: String

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    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    LinearRegressionIdentifiable
    Annotations
    @Since( "1.3.0" )
  106. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

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    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

    Attributes
    protected
    Definition Classes
    LinearRegressionParams → PredictorParams
  107. final def wait(): Unit

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

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  110. final val weightCol: Param[String]

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    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
  111. def write: MLWriter

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    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritableMLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from LinearRegressionParams

Inherited from HasLoss

Inherited from HasAggregationDepth

Inherited from HasSolver

Inherited from HasWeightCol

Inherited from HasStandardization

Inherited from HasFitIntercept

Inherited from HasTol

Inherited from HasMaxIter

Inherited from HasElasticNetParam

Inherited from HasRegParam

Inherited from Regressor[Vector, LinearRegression, LinearRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

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

getExpertParam

setExpertParam

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