Packages

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:

  • squaredError (a.k.a squared loss)
  • huber (a hybrid of squared error for relatively small errors and absolute error for relatively large ones, and we estimate the scale parameter from training data)

This supports multiple types of regularization:

  • none (a.k.a. ordinary least squares)
  • L2 (ridge regression)
  • L1 (Lasso)
  • L2 + L1 (elastic net)

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

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

Since 3.1.0, it supports stacking instances into blocks and using GEMV for better performance. The block size will be 1.0 MB, if param maxBlockSizeInMB is set 0.0 by default.

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

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

  1. new LinearRegression()
    Annotations
    @Since( "1.4.0" )
  2. new LinearRegression(uid: String)
    Annotations
    @Since( "1.3.0" )

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. final val aggregationDepth: IntParam

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

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

    Definition Classes
    HasAggregationDepth
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. final def clear(param: Param[_]): LinearRegression.this.type

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

    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
    LinearRegression → Predictor → Estimator → PipelineStage → Params
    Annotations
    @Since( "1.4.0" )
  10. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

    Copies param values from this instance to another instance for params shared by them.

    Copies param values from this instance to another instance for params shared by them.

    This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

    to

    the target instance, which should work with the same set of default Params as this source instance

    extra

    extra params to be copied to the target's paramMap

    returns

    the target instance with param values copied

    Attributes
    protected
    Definition Classes
    Params
  11. 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
  12. 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
  13. final val epsilon: DoubleParam

    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
    Definition Classes
    AnyRef
  15. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  16. 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
  17. def explainParams(): String

    Explains all params of this instance.

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

    Definition Classes
    Params
  18. def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validate the output instances with the given function.

    Attributes
    protected
    Definition Classes
    PredictorParams
  19. def extractInstances(dataset: Dataset[_]): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes
    protected
    Definition Classes
    PredictorParams
  20. def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]

    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
  21. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  22. 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
  23. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

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

    Fits a model to the input data.

    Fits a model to the input data.

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

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

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

    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()
  29. final val fitIntercept: BooleanParam

    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

    Definition Classes
    HasFitIntercept
  30. 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
  31. final def getAggregationDepth: Int

    Definition Classes
    HasAggregationDepth
  32. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  33. 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
  34. final def getElasticNetParam: Double

    Definition Classes
    HasElasticNetParam
  35. def getEpsilon: Double

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

    Definition Classes
    HasFeaturesCol
  37. final def getFitIntercept: Boolean

    Definition Classes
    HasFitIntercept
  38. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  39. final def getLoss: String

    Definition Classes
    HasLoss
  40. final def getMaxBlockSizeInMB: Double

    Definition Classes
    HasMaxBlockSizeInMB
  41. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  42. 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
  43. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  44. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  45. final def getRegParam: Double

    Definition Classes
    HasRegParam
  46. final def getSolver: String

    Definition Classes
    HasSolver
  47. final def getStandardization: Boolean

    Definition Classes
    HasStandardization
  48. final def getTol: Double

    Definition Classes
    HasTol
  49. final def getWeightCol: String

    Definition Classes
    HasWeightCol
  50. 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
  51. 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
  52. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  53. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  54. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  55. 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
  56. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  57. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  58. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  59. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  60. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  61. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  67. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  68. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. final val loss: Param[String]

    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" )
  73. final val maxBlockSizeInMB: DoubleParam

    Param for Maximum memory in MB for stacking input data into blocks.

    Param for Maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0..

    Definition Classes
    HasMaxBlockSizeInMB
  74. final val maxIter: IntParam

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

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

    Definition Classes
    HasMaxIter
  75. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  76. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  77. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  78. 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.

  79. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  80. final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes
    HasRegParam
  81. 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( ... )
  82. final def set(paramPair: ParamPair[_]): LinearRegression.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  85. 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" )
  86. final def setDefault(paramPairs: ParamPair[_]*): LinearRegression.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
  87. final def setDefault[T](param: Param[T], value: T): LinearRegression.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
  88. 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" )
  89. 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" )
  90. def setFeaturesCol(value: String): LinearRegression

    Definition Classes
    Predictor
  91. 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" )
  92. def setLabelCol(value: String): LinearRegression

    Definition Classes
    Predictor
  93. 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" )
  94. def setMaxBlockSizeInMB(value: Double): LinearRegression.this.type

    Sets the value of param maxBlockSizeInMB.

    Sets the value of param maxBlockSizeInMB. Default is 0.0, then 1.0 MB will be chosen.

    Annotations
    @Since( "3.1.0" )
  95. 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" )
  96. def setPredictionCol(value: String): LinearRegression

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

    Set the regularization parameter.

    Set the regularization parameter. Default is 0.0.

    Annotations
    @Since( "1.3.0" )
  98. 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" )
  99. 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.

  100. 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" )
  101. 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" )
  102. final val solver: Param[String]

    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" )
  103. final val standardization: BooleanParam

    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
  104. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  105. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  106. 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
  107. def train(dataset: Dataset[_]): LinearRegressionModel

    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
    LinearRegression → Predictor
  108. def transformSchema(schema: StructType): StructType

    Check transform validity and derive the output schema from the input schema.

    Check transform validity and derive the output schema from the input schema.

    We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

    Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

    Definition Classes
    Predictor → PipelineStage
  109. 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()
  110. 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
    LinearRegression → Identifiable
    Annotations
    @Since( "1.3.0" )
  111. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

    Validates and transforms the input schema with the provided param map.

    Validates and transforms the input schema with the provided param map.

    schema

    input schema

    fitting

    whether this is in fitting

    featuresDataType

    SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

    returns

    output schema

    Attributes
    protected
    Definition Classes
    LinearRegressionParams → PredictorParams
  112. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  113. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  114. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  115. 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
  116. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from LinearRegressionParams

Inherited from HasMaxBlockSizeInMB

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 Predictor[Vector, LinearRegression, LinearRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Estimator[LinearRegressionModel]

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