class LinearRegressionSummary extends Serializable
Linear regression results evaluated on a dataset.
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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.fitInterceptis set to true, then the last element returned corresponds to the intercept.- See also
LinearRegression.solver
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val
degreesOfFreedom: Long
Degrees of freedom
Degrees of freedom
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- @Since( "2.2.0" )
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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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- val featuresCol: String
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- val labelCol: String
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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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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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lazy val
numInstances: Long
Number of instances in DataFrame predictions
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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.fitInterceptis set to true, then the last element returned corresponds to the intercept.- See also
LinearRegression.solver
- val predictionCol: String
- val predictions: DataFrame
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val
r2: Double
Returns R2, the coefficient of determination.
Returns R2, the coefficient of determination. Reference: Wikipedia coefficient of determination
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val
r2adj: Double
Returns Adjusted R2, the adjusted coefficient of determination.
Returns Adjusted R2, the adjusted coefficient of determination. Reference: Wikipedia coefficient of determination
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- @Since( "2.3.0" )
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lazy val
residuals: DataFrame
Residuals (label - predicted value)
Residuals (label - predicted value)
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- @Since( "1.5.0" ) @transient()
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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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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.fitInterceptis set to true, then the last element returned corresponds to the intercept.- See also
LinearRegression.solver
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