GBMSupport

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

   

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2. final def ##(): Int

   

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

   

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4. def GBMModel[T <: Frame](train: T, test: T, response: String, modelId: String = "model", ntrees: Int = 50, depth: Int = 6, family: DistributionFamily = DistributionFamily.AUTO): GBMModel

   

   Create Gradient Boosting Model for the basic usage.

   Create Gradient Boosting Model for the basic usage. This method exposes the basic configuration of the model. If you need to specify some arguments which are not exposed, please use the DeepLearning model via Sparkling Water pipelines API or using the raw java API

   T

   H2O Frame Type

   train

   frame to train

   test

   test frame

   response

   response column

   modelId

   name of the model

   ntrees

   number of trees

   depth

   depth

   family

   distribution family

   returns

   Gradient Boosting Model

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   GBMSupport

5. final def asInstanceOf[T0]: T0

   

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

   

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9. def finalize(): Unit

   

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

    

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11. def hashCode(): Int

    

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12. final def isInstanceOf[T0]: Boolean

    

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14. final def notify(): Unit

    

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15. final def notifyAll(): Unit

    

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16. 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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