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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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. See also
LinearRegression.solver

val
degreesOfFreedom: Long
Degrees of freedom
Degrees of freedom
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 @Since( "2.2.0" )

lazy val
devianceResiduals: Array[Double]
The weighted residuals, the usual residuals rescaled by the square root of the instance weights.

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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.
 val featuresCol: String

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 val labelCol: String

val
meanAbsoluteError: Double
Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1norm loss.
Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1norm 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.

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.

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lazy val
numInstances: Long
Number of instances in DataFrame predictions

lazy val
pValues: Array[Double]
Twosided pvalue of estimated coefficients and intercept.
Twosided pvalue 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. See also
LinearRegression.solver
 val predictionCol: String
 val predictions: DataFrame

val
r2: Double
Returns R^{2}, the coefficient of determination.
Returns R^{2}, 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.

val
r2adj: Double
Returns Adjusted R^{2}, the adjusted coefficient of determination.
Returns Adjusted R^{2}, the adjusted coefficient of determination. Reference: Wikipedia coefficient of determination
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 @Since( "2.3.0" )
 Note
This ignores instance weights (setting all to 1.0) from
LinearRegression.weightCol
. This will change in later Spark versions.

lazy val
residuals: DataFrame
Residuals (label  predicted value)
Residuals (label  predicted value)
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 @Since( "1.5.0" ) @transient()

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.

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lazy val
tValues: Array[Double]
Tstatistic of estimated coefficients and intercept.
Tstatistic 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. See also
LinearRegression.solver

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