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[_]): LinearRegressionModel.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.  val coefficients: Vector

   Annotations

   @Since( "2.0.0" )

10.  def copy(extra: ParamMap): LinearRegressionModel

    Creates a copy of this instance with the same UID and some extra params.

    Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

    Definition Classes

    LinearRegressionModel → Model → Transformer → PipelineStage → Params

    Annotations

    @Since( "1.4.0" )

11.  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

12.  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

13.  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

14.  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" )

15.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

16.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

17.  def evaluate(dataset: Dataset[_]): LinearRegressionSummary

    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

    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

    Explains all params of this instance.

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

    Definition Classes

    Params

20.  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

21.  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

22.  final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes

    Params

23.  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

24.  final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes

    HasFeaturesCol

25.  def featuresDataType: DataType

    Returns the SQL DataType corresponding to the FeaturesType type parameter.

    Returns the SQL DataType corresponding to the FeaturesType type parameter.

    This is used by validateAndTransformSchema(). This workaround is needed since SQL has different APIs for Scala and Java.

    The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.

    Attributes

    protected

    Definition Classes

    PredictionModel

26.  def finalize(): Unit

    Attributes

    protected[lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

27.  final val fitIntercept: BooleanParam

    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]

    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
    Definition Classes

    HasAggregationDepth

30.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

31.  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

32.  final def getElasticNetParam: Double
    Definition Classes

    HasElasticNetParam

33.  def getEpsilon: Double
    Definition Classes

    LinearRegressionParams

    Annotations

    @Since( "2.3.0" )

34.  final def getFeaturesCol: String
    Definition Classes

    HasFeaturesCol

35.  final def getFitIntercept: Boolean
    Definition Classes

    HasFitIntercept

36.  final def getLabelCol: String
    Definition Classes

    HasLabelCol

37.  final def getLoss: String
    Definition Classes

    HasLoss

38.  final def getMaxBlockSizeInMB: Double
    Definition Classes

    HasMaxBlockSizeInMB

39.  final def getMaxIter: Int
    Definition Classes

    HasMaxIter

40.  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

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

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes

    Params

42.  final def getPredictionCol: String
    Definition Classes

    HasPredictionCol

43.  final def getRegParam: Double
    Definition Classes

    HasRegParam

44.  final def getSolver: String
    Definition Classes

    HasSolver

45.  final def getStandardization: Boolean
    Definition Classes

    HasStandardization

46.  final def getTol: Double
    Definition Classes

    HasTol

47.  final def getWeightCol: String
    Definition Classes

    HasWeightCol

48.  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

49.  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

50.  def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes

    Model

51.  def hasSummary: Boolean

    Indicates whether a training summary exists for this model instance.

    Indicates whether a training summary exists for this model instance.

    Definition Classes

    HasTrainingSummary

    Annotations

    @Since( "3.0.0" )

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.  val intercept: Double

    Annotations

    @Since( "1.3.0" )

56.  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

57.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

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

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes

    Params

59.  def isTraceEnabled(): Boolean

    Attributes

    protected

    Definition Classes

    Logging

60.  final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes

    HasLabelCol

61.  def log: Logger

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

63.  def logDebug(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

65.  def logError(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

67.  def logInfo(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

68.  def logName: String

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

70.  def logTrace(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

72.  def logWarning(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

73.  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" )

74.  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

75.  final val maxIter: IntParam

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

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

    Definition Classes

    HasMaxIter

76.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

77.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

78.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

79.  val numFeatures: Int

    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

    LinearRegressionModel → PredictionModel

80.  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.

81.  var parent: Estimator[LinearRegressionModel]

    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.

82.  def predict(features: Vector): Double

    Predict label for the given features.

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

    Definition Classes

    LinearRegressionModel → PredictionModel

83.  final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes

    HasPredictionCol

84.  final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes

    HasRegParam

85.  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( ... )

86.  val scale: Double

    Annotations

    @Since( "2.3.0" )

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

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes

    protected

    Definition Classes

    Params

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

    Sets a parameter (by name) in the embedded param map.

    Sets a parameter (by name) in the embedded param map.

    Attributes

    protected

    Definition Classes

    Params

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

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes

    Params

90.  final def setDefault(paramPairs: ParamPair[_]*): LinearRegressionModel.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

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

    Sets a default value for a param.

    Sets a default value for a param.

    param

    param to set the default value. Make sure that this param is initialized before this method gets called.

    value

    the default value

    Attributes

    protected

    Definition Classes

    Params

92.  def setFeaturesCol(value: String): LinearRegressionModel
    Definition Classes

    PredictionModel

93.  def setParent(parent: Estimator[LinearRegressionModel]): LinearRegressionModel

    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes

    Model

94.  def setPredictionCol(value: String): LinearRegressionModel
    Definition Classes

    PredictionModel

95.  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" )

96.  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

97.  def summary: LinearRegressionTrainingSummary

    Gets summary (e.g.

    Gets summary (e.g. residuals, mse, r-squared ) of model on training set. An exception is thrown if hasSummary is false.

    Definition Classes

    LinearRegressionModel → HasTrainingSummary

    Annotations

    @Since( "1.5.0" )

98.  final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes

    AnyRef

99.  def toString(): String

    Definition Classes

    LinearRegressionModel → Identifiable → AnyRef → Any

    Annotations

    @Since( "3.0.0" )

100.  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

101.  def transform(dataset: Dataset[_]): DataFrame

     Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.

     Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.

     dataset

     input dataset

     returns

     transformed dataset with predictionCol of type Double

     Definition Classes

     PredictionModel → Transformer

102.  def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

     Transforms the dataset with provided parameter map as additional parameters.

     Transforms the dataset with provided parameter map as additional parameters.

     dataset

     input dataset

     paramMap

     additional parameters, overwrite embedded params

     returns

     transformed dataset

     Definition Classes

     Transformer

     Annotations

     @Since( "2.0.0" )

103.  def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

     Transforms the dataset with optional parameters

     Transforms the dataset with optional parameters

     dataset

     input dataset

     firstParamPair

     the first param pair, overwrite embedded params

     otherParamPairs

     other param pairs, overwrite embedded params

     returns

     transformed dataset

     Definition Classes

     Transformer

     Annotations

     @Since( "2.0.0" ) @varargs()

104.  def transformImpl(dataset: Dataset[_]): DataFrame

     Attributes

     protected

     Definition Classes

     PredictionModel

105.  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

     PredictionModel → PipelineStage

106.  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()

107.  val uid: String

     An immutable unique ID for the object and its derivatives.

     An immutable unique ID for the object and its derivatives.

     Definition Classes

     LinearRegressionModel → Identifiable

     Annotations

     @Since( "1.4.0" )

108.  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

109.  final def wait(): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

110.  final def wait(arg0: Long, arg1: Int): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

111.  final def wait(arg0: Long): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... ) @native()

112.  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

113.  def write: GeneralMLWriter

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

     Returns a org.apache.spark.ml.util.GeneralMLWriter 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

     LinearRegressionModel → GeneralMLWritable → MLWritable

     Annotations

     @Since( "1.6.0" )