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# LinearRegressionSummary 

#### class LinearRegressionSummary extends Serializable

Linear regression results evaluated on a dataset.

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

1. final def !=(arg0: Any): Boolean
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5. def clone()
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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

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@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. final def getClass(): Class[_]
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15. final def isInstanceOf[T0]: Boolean
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16. val labelCol: String
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.

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@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.

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@Since( "1.5.0" )
19. final def ne(arg0: AnyRef): Boolean
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21. final def notifyAll(): Unit
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22. lazy val numInstances: Long

Number of instances in DataFrame predictions

23. 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

24. val predictionCol: String
25. val predictions
26. val r2: Double

Returns R2, the coefficient of determination.

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

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@Since( "1.5.0" )

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

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@Since( "2.3.0" )
28. lazy val residuals

Residuals (label - predicted value)

Residuals (label - predicted value)

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@Since( "1.5.0" ) @transient()
29. 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" )
30. final def synchronized[T0](arg0: ⇒ T0): T0
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31. 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

32. def toString(): String
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33. final def wait(arg0: Long, arg1: Int): Unit
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### Deprecated Value Members

1. def finalize(): Unit
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