org.apache.spark.ml.regression

LinearRegression

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

:: Experimental :: Linear regression.

The learning objective is to minimize the squared error, with regularization. The specific squared error loss function used is: L = 1/2n ||A coefficients - y||2

This support multiple types of regularization:

Annotations
@Since( "1.3.0" ) @Experimental()
Source
LinearRegression.scala
Linear Supertypes
DefaultParamsWritable, MLWritable, LinearRegressionParams, 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. HasSolver
  6. HasWeightCol
  7. HasStandardization
  8. HasFitIntercept
  9. HasTol
  10. HasMaxIter
  11. HasElasticNetParam
  12. HasRegParam
  13. Regressor
  14. Predictor
  15. PredictorParams
  16. HasPredictionCol
  17. HasFeaturesCol
  18. HasLabelCol
  19. Estimator
  20. PipelineStage
  21. Logging
  22. Params
  23. Serializable
  24. Serializable
  25. Identifiable
  26. AnyRef
  27. Any
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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: AnyRef): Boolean

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

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

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  5. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  6. final def ==(arg0: Any): Boolean

    Definition Classes
    Any
  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  8. 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
  9. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  10. 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.

    Definition Classes
    LinearRegressionPredictorEstimatorPipelineStageParams
    Annotations
    @Since( "1.4.0" )
    See also

    defaultCopy()

  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 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.

    Definition Classes
    Params
    See also

    explainParam()

  18. def extractLabeledPoints(dataset: DataFrame): 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
  19. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  20. 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.

    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 < user-supplied values < extra.

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

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  22. def finalize(): Unit

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

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    PredictorEstimator
  24. def fit(dataset: DataFrame, paramMaps: Array[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
  25. def fit(dataset: DataFrame, 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
  26. def fit(dataset: DataFrame, 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
    @varargs()
  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 getClass(): Class[_]

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

    Definition Classes
    HasElasticNetParam
  32. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  33. final def getFitIntercept: Boolean

    Definition Classes
    HasFitIntercept
  34. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  35. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  36. 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
  37. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  38. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  39. final def getRegParam: Double

    Definition Classes
    HasRegParam
  40. final def getSolver: String

    Definition Classes
    HasSolver
  41. final def getStandardization: Boolean

    Definition Classes
    HasStandardization
  42. final def getTol: Double

    Definition Classes
    HasTol
  43. final def getWeightCol: String

    Definition Classes
    HasWeightCol
  44. 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
  45. 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
  46. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  47. 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
  48. final def isInstanceOf[T0]: Boolean

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

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  50. def isTraceEnabled(): Boolean

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

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  52. def log: Logger

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

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

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

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

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

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

    Attributes
    protected
    Definition Classes
    Logging
  59. def logName: String

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

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

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

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

    Attributes
    protected
    Definition Classes
    Logging
  64. final val maxIter: IntParam

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

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

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

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

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

    Definition Classes
    AnyRef
  68. 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.

    Note: Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

    Definition Classes
    Params
  69. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  70. final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes
    HasRegParam
  71. 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( ... )
  72. 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
  73. 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
  74. 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
  75. 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
  76. 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
  77. 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 0 < alpha < 1, the penalty is a combination of L1 and L2. Default is 0.0 which is an L2 penalty.

    Annotations
    @Since( "1.4.0" )
  78. def setFeaturesCol(value: String): LinearRegression

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

    Set if we should fit the intercept Default is true.

    Set if we should fit the intercept Default is true.

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

    Definition Classes
    Predictor
  81. 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" )
  82. def setPredictionCol(value: String): LinearRegression

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

    Set the regularization parameter.

    Set the regularization parameter. Default is 0.0.

    Annotations
    @Since( "1.3.0" )
  84. 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. The default value is "auto" which means that the solver algorithm is selected automatically.

    Annotations
    @Since( "1.6.0" )
  85. 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. Note that 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. Default is true.

    Annotations
    @Since( "1.5.0" )
  86. 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" )
  87. 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 empty, all instances are treated equally (weight 1.0). Default is empty, so all instances have weight one.

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

    Param for the solver algorithm for optimization.

    Param for the solver algorithm for optimization. If this is not set or empty, default value is 'auto'..

    Definition Classes
    HasSolver
  89. 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
  90. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  91. def toString(): String

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

    Param for the convergence tolerance for iterative algorithms.

    Param for the convergence tolerance for iterative algorithms.

    Definition Classes
    HasTol
  93. def train(dataset: DataFrame): 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
    LinearRegressionPredictor
  94. def transformSchema(schema: StructType): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema.

    Definition Classes
    PredictorPipelineStage
  95. 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()
  96. 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
    LinearRegressionIdentifiable
    Annotations
    @Since( "1.3.0" )
  97. 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., org.apache.spark.mllib.linalg.VectorUDT for vector features.

    returns

    output schema

    Attributes
    protected
    Definition Classes
    PredictorParams
  98. def validateParams(): Unit

    Validates parameter values stored internally.

    Validates parameter values stored internally. Raise an exception if any parameter value is invalid.

    This only needs to check for interactions between parameters. Parameter value checks which do not depend on other parameters are handled by Param.validate(). This method does not handle input/output column parameters; those are checked during schema validation.

    Definition Classes
    Params
  99. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  102. 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
  103. 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 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

Parameter setters

Parameter getters