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: Any): Boolean

    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

    Definition Classes
    AnyRef → Any

  3. final def ==(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  4. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  5. def clone(): AnyRef

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

  6. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  7. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  8. def explainParams(): String

    Returns the documentation of all params.

    Returns the documentation of all params.

    Definition Classes
    Params

  9. def finalize(): Unit

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

  10. 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
    StandardScaler → Estimator

  11. 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

  12. 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

  13. 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()

  14. 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

  15. 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()

  16. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any

  17. def getInputCol: String

    Definition Classes
    HasInputCol

  18. def getOutputCol: String

    Definition Classes
    HasOutputCol

  19. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  20. val inputCol: Param[String]

    param for input column name

    param for input column name

    Definition Classes
    HasInputCol

  21. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  22. def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params

  23. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging

  24. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

  26. def logDebug(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

  28. def logError(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

  30. def logInfo(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  31. def logName: String

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

  33. def logTrace(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

  35. def logWarning(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  36. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  37. final def notify(): Unit

    Definition Classes
    AnyRef

  38. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  39. val outputCol: Param[String]

    param for output column name

    param for output column name

    Definition Classes
    HasOutputCol

  40. val paramMap: ParamMap

    Internal param map.

    Internal param map.

    Attributes
    protected
    Definition Classes
    Params

  41. def params: Array[Param[_]]

    Returns all params.

    Returns all params.

    Definition Classes
    Params

  42. def setInputCol(value: String): StandardScaler.this.type

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

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

    Definition Classes
    AnyRef

  45. def toString(): String

    Definition Classes
    AnyRef → Any

  46. 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

  47. 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

  48. 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

  49. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  51. 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