 # LinearRegressionSummary

#### class LinearRegressionSummary extends Serializable

:: Experimental :: 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: AnyRef): Boolean

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8. #### lazy val coefficientStandardErrors: Array[Double]

Standard error of estimated coefficients and intercept.

9. #### lazy val devianceResiduals: Array[Double]

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

10. #### final def eq(arg0: AnyRef): Boolean

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11. #### def equals(arg0: Any): Boolean

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12. #### val explainedVariance: Double

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.

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@Since( "1.5.0" )
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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.

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

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@Since( "1.5.0" )
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.

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

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@Since( "1.5.0" )

21. #### final def ne(arg0: AnyRef): Boolean

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22. #### final def notify(): Unit

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24. #### lazy val numInstances: Long

Number of instances in DataFrame predictions

25. #### lazy val pValues: Array[Double]

Two-sided p-value of estimated coefficients and intercept.

27. #### val predictions: DataFrame

predictions outputted by the model's transform method.

28. #### val r2: Double

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.

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@Since( "1.5.0" )
29. #### lazy val residuals: DataFrame

Residuals (label - predicted value)

Residuals (label - predicted value)

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@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.

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

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@Since( "1.5.0" )
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.

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