class LinearRegressionTrainingSummary extends LinearRegressionSummary
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" )
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 LinearRegression.scala
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 LinearRegressionTrainingSummary
 LinearRegressionSummary
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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. Definition Classes
 LinearRegressionSummary
 See also
LinearRegression.solver

val
degreesOfFreedom: Long
Degrees of freedom
Degrees of freedom
 Definition Classes
 LinearRegressionSummary
 Annotations
 @Since( "2.2.0" )

lazy val
devianceResiduals: Array[Double]
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

final
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eq(arg0: AnyRef): Boolean
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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
 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.

val
featuresCol: String
 Definition Classes
 LinearRegressionSummary

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val
labelCol: String
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 LinearRegressionSummary

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.
 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.

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.
 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.

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lazy val
numInstances: Long
Number of instances in DataFrame predictions
Number of instances in DataFrame predictions
 Definition Classes
 LinearRegressionSummary
 val objectiveHistory: Array[Double]

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. Definition Classes
 LinearRegressionSummary
 See also
LinearRegression.solver

val
predictionCol: String
 Definition Classes
 LinearRegressionSummary

val
predictions: DataFrame
 Definition Classes
 LinearRegressionSummary

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

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
 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.

lazy val
residuals: DataFrame
Residuals (label  predicted value)
Residuals (label  predicted value)
 Definition Classes
 LinearRegressionSummary
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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.
 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.

final
def
synchronized[T0](arg0: ⇒ T0): T0
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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. Definition Classes
 LinearRegressionSummary
 See also
LinearRegression.solver

def
toString(): String
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val
totalIterations: Int
Number of training iterations until termination
Number of training iterations until termination
This value is only available when using the "lbfgs" solver.
 Annotations
 @Since( "1.5.0" )
 See also
LinearRegression.solver

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