Class

org.apache.spark.ml.regression

LinearRegressionTrainingSummary

Related Doc: package regression

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class LinearRegressionTrainingSummary extends LinearRegressionSummary

:: Experimental :: 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" ) @Experimental()
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

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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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    Attributes
    protected[java.lang]
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    @throws( ... )
  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.

    Definition Classes
    LinearRegressionSummary
    See also

    LinearRegression.solver

  7. lazy val devianceResiduals: Array[Double]

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

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  9. def equals(arg0: Any): Boolean

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  10. 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: http://en.wikipedia.org/wiki/Explained_variation

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

    Definition Classes
    LinearRegressionSummary
    Annotations
    @Since( "1.5.0" )
  11. val featuresCol: String

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

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

    Definition Classes
    LinearRegressionSummary
  12. def finalize(): Unit

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    @throws( classOf[java.lang.Throwable] )
  13. final def getClass(): Class[_]

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. val labelCol: String

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

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

    Definition Classes
    LinearRegressionSummary
  17. 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.

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

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

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

    Definition Classes
    LinearRegressionSummary
    Annotations
    @Since( "1.5.0" )
  19. final def ne(arg0: AnyRef): Boolean

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  20. final def notify(): Unit

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  21. final def notifyAll(): Unit

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  22. lazy val numInstances: Long

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    Number of instances in DataFrame predictions

    Number of instances in DataFrame predictions

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

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    objective function (scaled loss + regularization) at each iteration.

  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.

    Definition Classes
    LinearRegressionSummary
    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.

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

    Definition Classes
    LinearRegressionSummary
  26. val predictions: DataFrame

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    predictions output by the model's transform method.

    predictions output by the model's transform method.

    Definition Classes
    LinearRegressionSummary
  27. val r2: Double

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    Returns R2, the coefficient of determination.

    Returns R2, the coefficient of determination. Reference: http://en.wikipedia.org/wiki/Coefficient_of_determination

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

    Definition Classes
    LinearRegressionSummary
    Annotations
    @Since( "1.5.0" )
  28. lazy val residuals: DataFrame

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    Residuals (label - predicted value)

    Residuals (label - predicted value)

    Definition Classes
    LinearRegressionSummary
    Annotations
    @Since( "1.5.0" )
  29. 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.

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

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

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  31. 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.

    Definition Classes
    LinearRegressionSummary
    See also

    LinearRegression.solver

  32. def toString(): String

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  33. val totalIterations: Int

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    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

  34. final def wait(): Unit

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

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

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Deprecated Value Members

  1. val model: LinearRegressionModel

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    Definition Classes
    LinearRegressionSummary
    Annotations
    @deprecated
    Deprecated

    (Since version 2.0.0) The model field is deprecated and will be removed in 2.1.0.

Inherited from LinearRegressionSummary

Inherited from Serializable

Inherited from Serializable

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