ml.combust.mleap.core.feature

OneHotEncoderModel

case class OneHotEncoderModel(size: Int) extends Serializable with Product

Class for a one hot encoder model.

One hot encoders are used to vectorize nominal features in preparation for models such as linear regression or logistic regression where binary and not multinomial features are supported in the feature vector.

size

size of the output one hot vectors

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Instance Constructors

  1. new OneHotEncoderModel(size: Int)

    size

    size of the output one hot vectors

Value Members

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

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

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  3. final def ##(): Int

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

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

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  6. def apply(label: Double): Vector

    Turn a labeled feature into a one hot vector.

    Turn a labeled feature into a one hot vector.

    label

    label to convert to a vector

    returns

    one hot vector representation of label

  7. final def asInstanceOf[T0]: T0

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  8. def clone(): AnyRef

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

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  10. def finalize(): Unit

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

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  12. final def isInstanceOf[T0]: Boolean

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

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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  16. val size: Int

    size of the output one hot vectors

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

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  18. final def wait(): Unit

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

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

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