 # LinearRegressionSummary

### Related Doc: package regression

#### class LinearRegressionSummary extends Serializable

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

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@Since( "1.5.0" ) @Experimental()
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LinearRegression.scala
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### Value Members

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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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.

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" )
Note

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

12. #### val featuresCol: String

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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15. #### def hashCode(): Int

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16. #### final def isInstanceOf[T0]: Boolean

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17. #### val labelCol: String

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

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" )
Note

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

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.

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

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.

LinearRegression.solver

25. #### val predictionCol: String

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

26. #### val predictions: DataFrame

predictions output by the model's transform method.

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" )
Note

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

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" )
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

Residuals (label - predicted value)

Residuals (label - predicted value)

Annotations
@Since( "1.5.0" )
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.

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]

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.

LinearRegression.solver

33. #### def toString(): String

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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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