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org.apache.spark.ml.regression

LinearRegressionTrainingSummary

class LinearRegressionTrainingSummary extends LinearRegressionSummary

Linear regression training results. Currently, the training summary ignores the training weights except for the objective trace.

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@Since( "1.5.0" )
Source
LinearRegression.scala
Linear Supertypes
LinearRegressionSummary, Serializable, Serializable, AnyRef, Any
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  1. LinearRegressionTrainingSummary
  2. LinearRegressionSummary
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  1. final def !=(arg0: Any): Boolean
    Definition Classes
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  2. final def ##(): Int
    Definition Classes
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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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    protected[lang]
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    @throws( ... ) @native() @IntrinsicCandidate()
  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.

    Definition Classes
    LinearRegressionSummary
    See also

    LinearRegression.solver

  7. val degreesOfFreedom: Long

    Degrees of freedom

    Degrees of freedom

    Definition Classes
    LinearRegressionSummary
    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.

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

    Definition Classes
    LinearRegressionSummary
  9. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  10. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  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

    Definition Classes
    LinearRegressionSummary
    Annotations
    @Since( "1.5.0" )
  12. val featuresCol: String
    Definition Classes
    LinearRegressionSummary
  13. final def getClass(): Class[_]
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    @native() @IntrinsicCandidate()
  14. def hashCode(): Int
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    @native() @IntrinsicCandidate()
  15. final def isInstanceOf[T0]: Boolean
    Definition Classes
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  16. val labelCol: String
    Definition Classes
    LinearRegressionSummary
  17. 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.

    Definition Classes
    LinearRegressionSummary
    Annotations
    @Since( "1.5.0" )
  18. 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.

    Definition Classes
    LinearRegressionSummary
    Annotations
    @Since( "1.5.0" )
  19. final def ne(arg0: AnyRef): Boolean
    Definition Classes
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  20. final def notify(): Unit
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    @native() @IntrinsicCandidate()
  21. final def notifyAll(): Unit
    Definition Classes
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    @native() @IntrinsicCandidate()
  22. lazy val numInstances: Long

    Number of instances in DataFrame predictions

    Number of instances in DataFrame predictions

    Definition Classes
    LinearRegressionSummary
  23. val objectiveHistory: Array[Double]
  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.

    Definition Classes
    LinearRegressionSummary
    See also

    LinearRegression.solver

  25. val predictionCol: String
    Definition Classes
    LinearRegressionSummary
  26. val predictions: DataFrame
    Definition Classes
    LinearRegressionSummary
  27. val r2: Double

    Returns R2, the coefficient of determination.

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

    Definition Classes
    LinearRegressionSummary
    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

    Definition Classes
    LinearRegressionSummary
    Annotations
    @Since( "2.3.0" )
  29. lazy val residuals: DataFrame

    Residuals (label - predicted value)

    Residuals (label - predicted value)

    Definition Classes
    LinearRegressionSummary
    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.

    Definition Classes
    LinearRegressionSummary
    Annotations
    @Since( "1.5.0" )
  31. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  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.

    Definition Classes
    LinearRegressionSummary
    See also

    LinearRegression.solver

  33. def toString(): String
    Definition Classes
    AnyRef → Any
  34. val totalIterations: Int

    Number of training iterations until termination

    Number of training iterations until termination

    This value is only available when using the "l-bfgs" solver.

    Annotations
    @Since( "1.5.0" )
    See also

    LinearRegression.solver

  35. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
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    @throws( ... )
  36. final def wait(arg0: Long): Unit
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  37. final def wait(): Unit
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    @throws( ... )

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  1. def finalize(): Unit
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    Deprecated

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