Class/Object

com.microsoft.ml.spark

TrainClassifier

Related Docs: object TrainClassifier | package spark

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class TrainClassifier extends Estimator[TrainedClassifierModel] with AutoTrainer[TrainedClassifierModel]

Trains a classification model. Featurizes the given data into a vector of doubles.

Note the behavior of the reindex and labels parameters, the parameters interact as:

reindex -> false labels -> false (Empty) Assume all double values, don't use metadata, assume natural ordering

reindex -> true labels -> false (Empty) Index, use natural ordering of string indexer

reindex -> false labels -> true (Specified) Assume user knows indexing, apply label values. Currently only string type supported.

reindex -> true labels -> true (Specified) Validate labels matches column type, try to recast to label type, reindex label column

The currently supported classifiers are: Logistic Regression Classifier Decision Tree Classifier Random Forest Classifier Gradient Boosted Trees Classifier Naive Bayes Classifier Multilayer Perceptron Classifier In addition to any generic learner that inherits from Predictor.

Annotations
@InternalWrapper()
Linear Supertypes
AutoTrainer[TrainedClassifierModel], HasFeaturesCol, ComplexParamsWritable, MLWritable, HasLabelCol, Wrappable, Estimator[TrainedClassifierModel], PipelineStage, org.apache.spark.internal.Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. TrainClassifier
  2. AutoTrainer
  3. HasFeaturesCol
  4. ComplexParamsWritable
  5. MLWritable
  6. HasLabelCol
  7. Wrappable
  8. Estimator
  9. PipelineStage
  10. Logging
  11. Params
  12. Serializable
  13. Serializable
  14. Identifiable
  15. AnyRef
  16. Any
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Visibility
  1. Public
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Instance Constructors

  1. new TrainClassifier()

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  2. new TrainClassifier(uid: String)

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Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

  3. final def $[T](param: Param[T]): T

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    Attributes
    protected
    Definition Classes
    Params

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

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    Definition Classes
    AnyRef → Any

  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  6. final def clear(param: Param[_]): TrainClassifier.this.type

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    Definition Classes
    Params

  7. def clone(): AnyRef

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

  8. def convertLabel(dataset: Dataset[_], labelColumn: String, labelValues: Option[Array[_]]): (DataFrame, Option[Array[_]])

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  9. def copy(extra: ParamMap): Estimator[TrainedClassifierModel]

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    Definition Classes
    TrainClassifier → Estimator → PipelineStage → Params

  10. def copyValues[T <: Params](to: T, extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params

  11. final def defaultCopy[T <: Params](extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params

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

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    Definition Classes
    AnyRef

  13. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  14. def explainParam(param: Param[_]): String

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    Definition Classes
    Params

  15. def explainParams(): String

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    Definition Classes
    Params

  16. final def extractParamMap(): ParamMap

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    Definition Classes
    Params

  17. final def extractParamMap(extra: ParamMap): ParamMap

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    Definition Classes
    Params

  18. val featuresCol: Param[String]

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    The name of the features column

    The name of the features column

    Definition Classes
    HasFeaturesCol

  19. def finalize(): Unit

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

  20. def fit(dataset: Dataset[_]): TrainedClassifierModel

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    Fits the classification model.

    Fits the classification model.

    dataset

    The input dataset to train.

    returns

    The trained classification model.

    Definition Classes
    TrainClassifier → Estimator

  21. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[TrainedClassifierModel]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )

  22. def fit(dataset: Dataset[_], paramMap: ParamMap): TrainedClassifierModel

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )

  23. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): TrainedClassifierModel

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()

  24. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params

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

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    Definition Classes
    AnyRef → Any

  26. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params

  27. def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol

  28. def getFeaturizeParams: (Boolean, Boolean, Int)

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  29. def getLabelCol: String

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    Definition Classes
    HasLabelCol

  30. def getLabels: Array[String]

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  31. def getModel: Estimator[_ <: Model[_]]

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    Definition Classes
    AutoTrainer

  32. def getNumFeatures: Int

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    Definition Classes
    AutoTrainer

  33. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params

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

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    Definition Classes
    Params

  35. def getReindexLabel: Boolean

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  36. final def hasDefault[T](param: Param[T]): Boolean

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    Definition Classes
    Params

  37. def hasParam(paramName: String): Boolean

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    Definition Classes
    Params

  38. def hashCode(): Int

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    Definition Classes
    AnyRef → Any

  39. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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    Attributes
    protected
    Definition Classes
    Logging

  40. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  41. final def isDefined(param: Param[_]): Boolean

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    Definition Classes
    Params

  42. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

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

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    Definition Classes
    Params

  44. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging

  45. val labelCol: Param[String]

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    The name of the label column

    The name of the label column

    Definition Classes
    HasLabelCol

  46. val labels: StringArrayParam

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    Specifies the labels metadata on the column.

    Specifies the labels metadata on the column. See class documentation for how this parameter interacts with reindex labels parameter.

  47. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging

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

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    Attributes
    protected
    Definition Classes
    Logging

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

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    protected
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    Logging

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

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    Logging

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

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    protected
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    Logging

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

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    protected
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    Logging

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

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    protected
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    Logging

  54. def logName: String

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    protected
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    Logging

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

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    Attributes
    protected
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    Logging

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

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    protected
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    Logging

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

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    Attributes
    protected
    Definition Classes
    Logging

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

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    Attributes
    protected
    Definition Classes
    Logging

  59. val model: EstimatorParam

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    Model to run.

    Model to run. See doc on derived classes.

    Definition Classes
    AutoTrainer

  60. def modelDoc: String

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    Doc for model to run.

    Doc for model to run.

    Definition Classes
    TrainClassifier → AutoTrainer

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

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    Definition Classes
    AnyRef

  62. final def notify(): Unit

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    Definition Classes
    AnyRef

  63. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  64. val numFeatures: IntParam

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    Number of features to hash to

    Number of features to hash to

    Definition Classes
    AutoTrainer

  65. lazy val params: Array[Param[_]]

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    Definition Classes
    Params

  66. val reindexLabel: BooleanParam

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    Specifies whether to reindex the given label column.

    Specifies whether to reindex the given label column. See class documentation for how this parameter interacts with specified labels.

  67. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )

  68. final def set(paramPair: ParamPair[_]): TrainClassifier.this.type

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    Attributes
    protected
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    Params

  69. final def set(param: String, value: Any): TrainClassifier.this.type

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    Attributes
    protected
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    Params

  70. final def set[T](param: Param[T], value: T): TrainClassifier.this.type

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    Definition Classes
    Params

  71. final def setDefault(paramPairs: ParamPair[_]*): TrainClassifier.this.type

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    Attributes
    protected
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    Params

  72. final def setDefault[T](param: Param[T], value: T): TrainClassifier.this.type

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    Attributes
    protected
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    Params

  73. def setFeaturesCol(value: String): TrainClassifier.this.type

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    Definition Classes
    HasFeaturesCol

  74. def setLabelCol(value: String): TrainClassifier.this.type

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    Definition Classes
    HasLabelCol

  75. def setLabels(value: Array[String]): TrainClassifier.this.type

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  76. def setModel(value: Estimator[_ <: Model[_]]): TrainClassifier.this.type

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    Definition Classes
    AutoTrainer

  77. def setNumFeatures(value: Int): TrainClassifier.this.type

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    Definition Classes
    AutoTrainer

  78. def setReindexLabel(value: Boolean): TrainClassifier.this.type

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

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    Definition Classes
    AnyRef

  80. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any

  81. def transformSchema(schema: StructType): StructType

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    Definition Classes
    TrainClassifier → PipelineStage
    Annotations
    @DeveloperApi()

  82. def transformSchema(schema: StructType, logging: Boolean): StructType

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    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()

  83. val uid: String

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    Definition Classes
    TrainClassifier → Identifiable

  84. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  86. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  87. def write: MLWriter

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    Definition Classes
    ComplexParamsWritable → MLWritable

Inherited from AutoTrainer[TrainedClassifierModel]

Inherited from HasFeaturesCol

Inherited from ComplexParamsWritable

Inherited from MLWritable

Inherited from HasLabelCol

Inherited from Wrappable

Inherited from Estimator[TrainedClassifierModel]

Inherited from PipelineStage

Inherited from org.apache.spark.internal.Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters

Parameter setters

Parameter getters

Members