Class

org.apache.spark.ml.regression

LinearRegressionSummary

Related Doc: package regression

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class LinearRegressionSummary extends Serializable

:: Experimental :: Linear regression results evaluated on a dataset.

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@Since( "1.5.0" ) @Experimental()
Source
LinearRegression.scala
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  1. 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. lazy val coefficientStandardErrors: Array[Double]

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    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

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    Degrees of freedom

    Degrees of freedom

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    @Since( "2.2.0" )
  8. lazy val devianceResiduals: Array[Double]

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    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

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    Returns the explained variance regression score.

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

    Annotations
    @Since( "1.5.0" )
    Note

    This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.

  12. val featuresCol: String

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    Field in "predictions" which gives the features of each instance as a vector.

  13. def finalize(): Unit

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

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  16. final def isInstanceOf[T0]: Boolean

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  17. val labelCol: String

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    Field in "predictions" which gives the true label of each instance.

  18. val meanAbsoluteError: Double

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    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" )
    Note

    This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.

  19. val meanSquaredError: Double

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    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" )
    Note

    This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.

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

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  21. final def notify(): Unit

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  22. final def notifyAll(): Unit

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  23. lazy val numInstances: Long

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    Number of instances in DataFrame predictions

  24. lazy val pValues: Array[Double]

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    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

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    Field in "predictions" which gives the predicted value of the label at each instance.

  26. val predictions: DataFrame

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    predictions output by the model's transform method.

  27. val r2: Double

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    Returns R2, the coefficient of determination.

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

    Annotations
    @Since( "1.5.0" )
    Note

    This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.

  28. val r2adj: Double

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    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" )
    Note

    This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.

  29. lazy val residuals: DataFrame

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    Residuals (label - predicted value)

    Residuals (label - predicted value)

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    @Since( "1.5.0" )
  30. val rootMeanSquaredError: Double

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    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" )
    Note

    This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.

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

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  32. lazy val tValues: Array[Double]

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    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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