LinearRegression

Value Members

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. val MAX_FEATURES_FOR_NORMAL_SOLVER: Int

   

   When using LinearRegression.solver == "normal", the solver must limit the number of features to at most this number.

   When using LinearRegression.solver == "normal", the solver must limit the number of features to at most this number. The entire covariance matrix X^TX will be collected to the driver. This limit helps prevent memory overflow errors.

   Annotations

   @Since( "2.1.0" )

5. final def asInstanceOf[T0]: T0

   

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6. def clone(): AnyRef

   

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   protected[java.lang]

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

   

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8. def equals(arg0: Any): Boolean

   

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9. def finalize(): Unit

   

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   protected[java.lang]

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   @throws( classOf[java.lang.Throwable] )

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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13. def load(path: String): LinearRegression

    

    Reads an ML instance from the input path, a shortcut of read.load(path).

    Reads an ML instance from the input path, a shortcut of read.load(path).

    Definition Classes

    LinearRegression → MLReadable

    Annotations

    @Since( "1.6.0" )

    Note

    Implementing classes should override this to be Java-friendly.

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

    

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15. final def notify(): Unit

    

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16. final def notifyAll(): Unit

    

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17. def read: MLReader[LinearRegression]

    

    Returns an MLReader instance for this class.

    Returns an MLReader instance for this class.

    Definition Classes

    DefaultParamsReadable → MLReadable

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

    

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19. def toString(): String

    

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20. final def wait(): Unit

    

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

    

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

    

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