Class/Object

org.apache.spark.ml.regression

LinearRegressionModel

Related Docs: object LinearRegressionModel | package regression

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class LinearRegressionModel extends RegressionModel[Vector, LinearRegressionModel] with LinearRegressionParams with MLWritable

Model produced by LinearRegression.

Annotations
@Since( "1.3.0" )
Source
LinearRegression.scala
Linear Supertypes
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Inherited
  1. LinearRegressionModel
  2. MLWritable
  3. LinearRegressionParams
  4. HasLoss
  5. HasAggregationDepth
  6. HasSolver
  7. HasWeightCol
  8. HasStandardization
  9. HasFitIntercept
  10. HasTol
  11. HasMaxIter
  12. HasElasticNetParam
  13. HasRegParam
  14. RegressionModel
  15. PredictionModel
  16. PredictorParams
  17. HasPredictionCol
  18. HasFeaturesCol
  19. HasLabelCol
  20. Model
  21. Transformer
  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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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[_]): LinearRegressionModel.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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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. val coefficients: Vector

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    Annotations
    @Since( "2.0.0" )
  10. def copy(extra: ParamMap): LinearRegressionModel

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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
    LinearRegressionModelModelTransformerPipelineStageParams
    Annotations
    @Since( "1.4.0" )
  11. 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
  12. 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
  13. 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
  14. 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" )
  15. final def eq(arg0: AnyRef): Boolean

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

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    Definition Classes
    AnyRef → Any
  17. def evaluate(dataset: Dataset[_]): LinearRegressionSummary

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    Evaluates the model on a test dataset.

    Evaluates the model on a test dataset.

    dataset

    Test dataset to evaluate model on.

    Annotations
    @Since( "2.0.0" )
  18. 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
  19. def explainParams(): String

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    Explains all params of this instance.

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

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

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    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  21. 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
  22. final val featuresCol: Param[String]

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    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  23. def featuresDataType: DataType

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    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
  24. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  25. 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
  26. 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
  27. final def getAggregationDepth: Int

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

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    Definition Classes
    AnyRef → Any
  29. 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
  30. final def getElasticNetParam: Double

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    Definition Classes
    HasElasticNetParam
  31. def getEpsilon: Double

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

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    Definition Classes
    HasFeaturesCol
  33. final def getFitIntercept: Boolean

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    Definition Classes
    HasFitIntercept
  34. final def getLabelCol: String

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    Definition Classes
    HasLabelCol
  35. final def getLoss: String

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    Definition Classes
    HasLoss
  36. final def getMaxIter: Int

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    Definition Classes
    HasMaxIter
  37. 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
  38. 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
  39. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol
  40. final def getRegParam: Double

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    Definition Classes
    HasRegParam
  41. final def getSolver: String

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    Definition Classes
    HasSolver
  42. final def getStandardization: Boolean

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    Definition Classes
    HasStandardization
  43. final def getTol: Double

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    Definition Classes
    HasTol
  44. final def getWeightCol: String

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    Definition Classes
    HasWeightCol
  45. 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
  46. 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
  47. def hasParent: Boolean

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    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  48. def hasSummary: Boolean

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    Indicates whether a training summary exists for this model instance.

    Indicates whether a training summary exists for this model instance.

    Annotations
    @Since( "1.5.0" )
  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. val intercept: Double

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    Annotations
    @Since( "1.3.0" )
  53. 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
  54. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  55. 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
  56. def isTraceEnabled(): Boolean

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

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    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  58. def log: Logger

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

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    protected
    Definition Classes
    Logging
  60. def logDebug(msg: ⇒ String): Unit

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

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

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

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

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    protected
    Definition Classes
    Logging
  65. def logName: String

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  70. 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" )
  71. 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
  72. final def ne(arg0: AnyRef): Boolean

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

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

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    Definition Classes
    AnyRef
  75. val numFeatures: Int

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    Returns the number of features the model was trained on.

    Returns the number of features the model was trained on. If unknown, returns -1

    Definition Classes
    LinearRegressionModelPredictionModel
  76. 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.

  77. var parent: Estimator[LinearRegressionModel]

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    The parent estimator that produced this model.

    The parent estimator that produced this model.

    Definition Classes
    Model
    Note

    For ensembles' component Models, this value can be null.

  78. def predict(features: Vector): Double

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    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
    LinearRegressionModelPredictionModel
  79. final val predictionCol: Param[String]

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    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  80. final val regParam: DoubleParam

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    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes
    HasRegParam
  81. 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( ... )
  82. val scale: Double

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    Annotations
    @Since( "2.3.0" )
  83. final def set(paramPair: ParamPair[_]): LinearRegressionModel.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  84. final def set(param: String, value: Any): LinearRegressionModel.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
  85. final def set[T](param: Param[T], value: T): LinearRegressionModel.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
  86. final def setDefault(paramPairs: ParamPair[_]*): LinearRegressionModel.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
  87. final def setDefault[T](param: Param[T], value: T): LinearRegressionModel.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
  88. def setFeaturesCol(value: String): LinearRegressionModel

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

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    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

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

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    Definition Classes
    PredictionModel
  91. 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" )
  92. 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
  93. def summary: LinearRegressionTrainingSummary

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    Gets summary (e.g.

    Gets summary (e.g. residuals, mse, r-squared ) of model on training set. An exception is thrown if trainingSummary == None.

    Annotations
    @Since( "1.5.0" )
  94. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  95. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  96. 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
  97. def transform(dataset: Dataset[_]): DataFrame

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    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
    PredictionModelTransformer
  98. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

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    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
    Annotations
    @Since( "2.0.0" )
  99. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

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    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
    @Since( "2.0.0" ) @varargs()
  100. def transformImpl(dataset: Dataset[_]): DataFrame

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    Attributes
    protected
    Definition Classes
    PredictionModel
  101. 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
    PredictionModelPipelineStage
  102. 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()
  103. 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
    LinearRegressionModelIdentifiable
    Annotations
    @Since( "1.4.0" )
  104. 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
  105. final def wait(): Unit

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

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  108. 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
  109. def write: MLWriter

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    Returns a org.apache.spark.ml.util.MLWriter instance for this ML instance.

    Returns a org.apache.spark.ml.util.MLWriter instance for this ML instance.

    For LinearRegressionModel, this does NOT currently save the training summary. An option to save summary may be added in the future.

    This also does not save the parent currently.

    Definition Classes
    LinearRegressionModelMLWritable
    Annotations
    @Since( "1.6.0" )

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 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 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

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 getters