FrameMLUtils

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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. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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

   

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7. def equals(arg0: Any): Boolean

   

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8. def finalize(): Unit

   

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

   

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10. def hashCode(): Int

    

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11. final def isInstanceOf[T0]: Boolean

    

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12. def merge(agg1: Array[(Double, Int)], agg2: Array[(Double, Int)]): Array[(Double, Int)]

    

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

    

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17. def toLabeledPoints(frame: Frame, reponseColumn: String, nfeatures: Int, missingHandler: MissingValuesHandling, h2oContext: H2OContext, sqlContext: SQLContext): (RDD[LabeledPoint], Array[Double])

    

    *
 * Converts a H2O Frame into an RDD[LabeledPoint].

    *
 * Converts a H2O Frame into an RDD[LabeledPoint]. Assumes that the last column is the response column which will be used
 * as the label. All other columns will be mapped to a vector and used as features. Categorical columns will be mapped to their
 * numerical values.
 *
 * @param frame Input frame to be converted
 * @param reponseColumn Column which contains the labels
 * @param nfeatures Number of features we want to use
 * @param missingHandler Missing values strategy
 * @param h2oContext Current H2OContext
 * @param sqlContext Current SQLContext
 * @return Returns an equivalent RDD[LabeledPoint] and means for each column


18. def toString(): String

    

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19. final def wait(): Unit

    

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

    

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

    

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