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. val degreesOfFreedom: Long

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    Degrees of freedom

    Degrees of freedom

    Definition Classes
    LinearRegressionSummary
    Annotations
    @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.

    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

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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

    Definition Classes
    LinearRegressionSummary
    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.

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

    Definition Classes
    LinearRegressionSummary
  13. def finalize(): Unit

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

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  15. def hashCode(): Int

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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.

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

    Definition Classes
    LinearRegressionSummary
  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.

    Definition Classes
    LinearRegressionSummary
    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.

    Definition Classes
    LinearRegressionSummary
    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

    Number of instances in DataFrame predictions

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

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

  25. 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

  26. 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
  27. 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
  28. val r2: Double

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    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" )
    Note

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

  29. 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

    Definition Classes
    LinearRegressionSummary
    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.

  30. lazy val residuals: DataFrame

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    Residuals (label - predicted value)

    Residuals (label - predicted value)

    Definition Classes
    LinearRegressionSummary
    Annotations
    @Since( "1.5.0" )
  31. 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.

    Definition Classes
    LinearRegressionSummary
    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.

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

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  33. 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

  34. def toString(): String

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  35. 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

  36. final def wait(): Unit

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

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    @throws( ... )
  38. final def wait(arg0: Long): Unit

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