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.  final def asInstanceOf[T0]: T0

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5.  def clone(): AnyRef

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   @throws( ... ) @native()

6.  lazy val coefficientStandardErrors: Array[Double]

   Standard error of estimated coefficients and intercept.

   Standard error of estimated coefficients and intercept. This value is only available when using the "normal" solver.

   If LinearRegression.fitIntercept is set to true, then the last element returned corresponds to the intercept.

   See also

   LinearRegression.solver

7.  val degreesOfFreedom: Long

   Degrees of freedom

   Degrees of freedom

   Annotations

   @Since( "2.2.0" )

8.  lazy val devianceResiduals: Array[Double]

   The weighted residuals, the usual residuals rescaled by the square root of the instance weights.

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

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10.  def equals(arg0: Any): Boolean

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11.  val explainedVariance: Double

    Returns the explained variance regression score.

    Returns the explained variance regression score. explainedVariance = 1 - variance(y - \hat{y}) / variance(y) Reference: Wikipedia explain variation

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    @Since( "1.5.0" )

12.  val featuresCol: String
13.  def finalize(): Unit

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

14.  final def getClass(): Class[_]

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    @native()

15.  def hashCode(): Int

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16.  final def isInstanceOf[T0]: Boolean

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17.  val labelCol: String
18.  val meanAbsoluteError: Double

    Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.

    Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.

    Annotations

    @Since( "1.5.0" )

19.  val meanSquaredError: Double

    Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.

    Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.

    Annotations

    @Since( "1.5.0" )

20.  final def ne(arg0: AnyRef): Boolean

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21.  final def notify(): Unit

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    @native()

22.  final def notifyAll(): Unit

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23.  lazy val numInstances: Long

    Number of instances in DataFrame predictions

24.  lazy val pValues: Array[Double]

    Two-sided p-value of estimated coefficients and intercept.

    Two-sided p-value of estimated coefficients and intercept. This value is only available when using the "normal" solver.

    If LinearRegression.fitIntercept is set to true, then the last element returned corresponds to the intercept.

    See also

    LinearRegression.solver

25.  val predictionCol: String
26.  val predictions: DataFrame
27.  val r2: Double

    Returns R², the coefficient of determination.

    Returns R², the coefficient of determination. Reference: Wikipedia coefficient of determination

    Annotations

    @Since( "1.5.0" )

28.  val r2adj: Double

    Returns Adjusted R², the adjusted coefficient of determination.

    Returns Adjusted R², the adjusted coefficient of determination. Reference: Wikipedia coefficient of determination

    Annotations

    @Since( "2.3.0" )

29.  lazy val residuals: DataFrame

    Residuals (label - predicted value)

    Residuals (label - predicted value)

    Annotations

    @Since( "1.5.0" ) @transient()

30.  val rootMeanSquaredError: Double

    Returns the root mean squared error, which is defined as the square root of the mean squared error.

    Returns the root mean squared error, which is defined as the square root of the mean squared error.

    Annotations

    @Since( "1.5.0" )

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

    Definition Classes

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32.  lazy val tValues: Array[Double]

    T-statistic of estimated coefficients and intercept.

    T-statistic of estimated coefficients and intercept. This value is only available when using the "normal" solver.

    If LinearRegression.fitIntercept is set to true, then the last element returned corresponds to the intercept.

    See also

    LinearRegression.solver

33.  def toString(): String

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34.  final def wait(): Unit

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

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    @throws( ... )

36.  final def wait(arg0: Long): Unit

    Definition Classes

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    @throws( ... ) @native()