org.apache.spark.ml.feature

StandardScaler

class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerParams

:: AlphaComponent :: Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set.

Annotations
@AlphaComponent()
Linear Supertypes
StandardScalerParams, HasOutputCol, HasInputCol, Estimator[StandardScalerModel], Params, Identifiable, PipelineStage, Logging, Serializable, Serializable, AnyRef, Any
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Inherited
  1. StandardScaler
  2. StandardScalerParams
  3. HasOutputCol
  4. HasInputCol
  5. Estimator
  6. Params
  7. Identifiable
  8. PipelineStage
  9. Logging
  10. Serializable
  11. Serializable
  12. AnyRef
  13. Any
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Instance Constructors

  1. new StandardScaler()

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 ==(arg0: AnyRef): Boolean

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

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

    Definition Classes
    Any
  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. final def eq(arg0: AnyRef): Boolean

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

    Definition Classes
    AnyRef → Any
  10. def explainParams(): String

    Returns the documentation of all params.

    Returns the documentation of all params.

    Definition Classes
    Params
  11. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  12. def fit(dataset: SchemaRDD, paramMap: ParamMap): StandardScalerModel

    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

    returns

    fitted model

    Definition Classes
    StandardScalerEstimator
  13. def fit(dataset: JavaSchemaRDD, paramMaps: Array[ParamMap]): List[StandardScalerModel]

    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters.

    dataset

    input dataset

    paramMaps

    an array of parameter maps

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
  14. def fit(dataset: JavaSchemaRDD, paramMap: ParamMap): StandardScalerModel

    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

    returns

    fitted model

    Definition Classes
    Estimator
  15. def fit(dataset: JavaSchemaRDD, paramPairs: ParamPair[_]*): StandardScalerModel

    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

    paramPairs

    optional list of param pairs (overwrite embedded params)

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @varargs()
  16. def fit(dataset: SchemaRDD, paramMaps: Array[ParamMap]): Seq[StandardScalerModel]

    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 overwrite this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    an array of parameter maps

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
  17. def fit(dataset: SchemaRDD, paramPairs: ParamPair[_]*): StandardScalerModel

    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

    paramPairs

    optional list of param pairs (overwrite embedded params)

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @varargs()
  18. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  19. def getInputCol: String

    Definition Classes
    HasInputCol
  20. def getOutputCol: String

    Definition Classes
    HasOutputCol
  21. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  22. val inputCol: Param[String]

    param for input column name

    param for input column name

    Definition Classes
    HasInputCol
  23. final def isInstanceOf[T0]: Boolean

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

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  25. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging
  26. def log: Logger

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

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

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

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

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

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

    Attributes
    protected
    Definition Classes
    Logging
  33. def logName: String

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

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

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

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

    Attributes
    protected
    Definition Classes
    Logging
  38. final def ne(arg0: AnyRef): Boolean

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

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

    Definition Classes
    AnyRef
  41. val outputCol: Param[String]

    param for output column name

    param for output column name

    Definition Classes
    HasOutputCol
  42. val paramMap: ParamMap

    Internal param map.

    Internal param map.

    Attributes
    protected
    Definition Classes
    Params
  43. def params: Array[Param[_]]

    Returns all params.

    Returns all params.

    Definition Classes
    Params
  44. def setInputCol(value: String): StandardScaler.this.type

  45. def setOutputCol(value: String): StandardScaler.this.type

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

    Definition Classes
    AnyRef
  47. def toString(): String

    Definition Classes
    AnyRef → Any
  48. def transformSchema(schema: StructType, paramMap: ParamMap, logging: Boolean): StructType

    Derives the output schema from the input schema and parameters, optionally with logging.

    Derives the output schema from the input schema and parameters, optionally with logging.

    Attributes
    protected
    Definition Classes
    PipelineStage
  49. def validate(): Unit

    Validates parameter values stored internally.

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

    Definition Classes
    Params
  50. def validate(paramMap: ParamMap): Unit

    Validates parameter values stored internally plus the input parameter map.

    Validates parameter values stored internally plus the input parameter map. Raises an exception if any parameter is invalid.

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

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from StandardScalerParams

Inherited from HasOutputCol

Inherited from HasInputCol

Inherited from Estimator[StandardScalerModel]

Inherited from Params

Inherited from Identifiable

Inherited from PipelineStage

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped