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org.apache.spark.ml.regression

LinearRegressionSummary

class LinearRegressionSummary extends Serializable

Linear regression results evaluated on a dataset.

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@Since( "1.5.0" )
Source
LinearRegression.scala
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LinearRegressionTrainingSummary
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  1. final def !=(arg0: Any): Boolean
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  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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  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.

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    @Since( "1.5.0" )
  20. final def ne(arg0: AnyRef): Boolean
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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.

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    @Since( "1.5.0" )
  31. final def synchronized[T0](arg0: ⇒ T0): T0
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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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