Packages

class LinearRegressionModel extends RegressionModel[Vector, LinearRegressionModel] with LinearRegressionParams with GeneralMLWritable with HasTrainingSummary[LinearRegressionTrainingSummary]

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Inherited
  1. LinearRegressionModel
  2. HasTrainingSummary
  3. GeneralMLWritable
  4. MLWritable
  5. LinearRegressionParams
  6. HasMaxBlockSizeInMB
  7. HasLoss
  8. HasAggregationDepth
  9. HasSolver
  10. HasWeightCol
  11. HasStandardization
  12. HasFitIntercept
  13. HasTol
  14. HasMaxIter
  15. HasElasticNetParam
  16. HasRegParam
  17. RegressionModel
  18. PredictionModel
  19. PredictorParams
  20. HasPredictionCol
  21. HasFeaturesCol
  22. HasLabelCol
  23. Model
  24. Transformer
  25. PipelineStage
  26. Logging
  27. Params
  28. Serializable
  29. Serializable
  30. Identifiable
  31. AnyRef
  32. Any
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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() @IntrinsicCandidate()
  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. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

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

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  23. 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
  24. final val fitIntercept: BooleanParam

    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

    Definition Classes
    HasFitIntercept
  25. 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
  26. final def getAggregationDepth: Int

    Definition Classes
    HasAggregationDepth
  27. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @IntrinsicCandidate()
  28. 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
  29. final def getElasticNetParam: Double

    Definition Classes
    HasElasticNetParam
  30. def getEpsilon: Double

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

    Definition Classes
    HasFeaturesCol
  32. final def getFitIntercept: Boolean

    Definition Classes
    HasFitIntercept
  33. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  34. final def getLoss: String

    Definition Classes
    HasLoss
  35. final def getMaxBlockSizeInMB: Double

    Definition Classes
    HasMaxBlockSizeInMB
  36. final def getMaxIter: Int

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

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  39. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  40. final def getRegParam: Double

    Definition Classes
    HasRegParam
  41. final def getSolver: String

    Definition Classes
    HasSolver
  42. final def getStandardization: Boolean

    Definition Classes
    HasStandardization
  43. final def getTol: Double

    Definition Classes
    HasTol
  44. final def getWeightCol: String

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

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  48. 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" )
  49. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @IntrinsicCandidate()
  50. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  51. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  52. val intercept: Double
    Annotations
    @Since( "1.3.0" )
  53. 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
  54. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  55. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

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

    Param for label column name.

    Param for label column name.

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

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

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

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

  78. 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.

  79. 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
  80. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  81. final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes
    HasRegParam
  82. 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( ... )
  83. val scale: Double
    Annotations
    @Since( "2.3.0" )
  84. 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
  85. 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
  86. 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
  87. 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
  88. 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[ml]
    Definition Classes
    Params
  89. def setFeaturesCol(value: String): LinearRegressionModel

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

    Definition Classes
    PredictionModel
  92. 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" )
  93. 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
  94. 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" )
  95. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  96. def toString(): String
    Definition Classes
    LinearRegressionModel → Identifiable → AnyRef → Any
    Annotations
    @Since( "3.0.0" )
  97. 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
  98. 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
  99. 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" )
  100. 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()
  101. def transformImpl(dataset: Dataset[_]): DataFrame
    Attributes
    protected
    Definition Classes
    PredictionModel
  102. 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
  103. 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()
  104. 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" )
  105. 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
  106. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  107. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  108. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  109. 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
  110. 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" )

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

Inherited from HasTrainingSummary[LinearRegressionTrainingSummary]

Inherited from GeneralMLWritable

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 RegressionModel[Vector, LinearRegressionModel]

Inherited from PredictionModel[Vector, LinearRegressionModel]

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