class GeneralizedLinearRegressionTrainingSummary extends GeneralizedLinearRegressionSummary with Serializable
Summary of GeneralizedLinearRegression fitting and model.
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- @Since("2.0.0")
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- GeneralizedLinearRegression.scala
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- lazy val aic: Double
Akaike Information Criterion (AIC) for the fitted model.
Akaike Information Criterion (AIC) for the fitted model.
- Definition Classes
- GeneralizedLinearRegressionSummary
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- @Since("2.0.0")
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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 the underlying
WeightedLeastSquaresusing the "normal" solver.If
GeneralizedLinearRegression.fitInterceptis set to true, then the last element returned corresponds to the intercept.- Annotations
- @Since("2.0.0")
- lazy val degreesOfFreedom: Long
Degrees of freedom.
Degrees of freedom.
- Definition Classes
- GeneralizedLinearRegressionSummary
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- @Since("2.0.0")
- lazy val deviance: Double
The deviance for the fitted model.
The deviance for the fitted model.
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- GeneralizedLinearRegressionSummary
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- @Since("2.0.0")
- lazy val dispersion: Double
The dispersion of the fitted model.
The dispersion of the fitted model. It is taken as 1.0 for the "binomial" and "poisson" families, and otherwise estimated by the residual Pearson's Chi-Squared statistic (which is defined as sum of the squares of the Pearson residuals) divided by the residual degrees of freedom.
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- GeneralizedLinearRegressionSummary
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- val model: GeneralizedLinearRegressionModel
Private copy of model to ensure Params are not modified outside this class.
Private copy of model to ensure Params are not modified outside this class. Coefficients is not a deep copy, but that is acceptable.
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- GeneralizedLinearRegressionSummary
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predictionCol must be set correctly before the value of model is set, and model must be set before predictions is set!
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- lazy val nullDeviance: Double
The deviance for the null model.
The deviance for the null model.
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- GeneralizedLinearRegressionSummary
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- @Since("2.0.0")
- lazy val numInstances: Long
Number of instances in DataFrame predictions.
Number of instances in DataFrame predictions.
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- GeneralizedLinearRegressionSummary
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- @Since("2.2.0")
- val numIterations: Int
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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 the underlying
WeightedLeastSquaresusing the "normal" solver.If
GeneralizedLinearRegression.fitInterceptis set to true, then the last element returned corresponds to the intercept.- Annotations
- @Since("2.0.0")
- val predictionCol: String
Field in "predictions" which gives the predicted value of each instance.
Field in "predictions" which gives the predicted value of each instance. This is set to a new column name if the original model's
predictionColis not set.- Definition Classes
- GeneralizedLinearRegressionSummary
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- @Since("2.0.0")
- val predictions: DataFrame
Predictions output by the model's
transformmethod.Predictions output by the model's
transformmethod.- Definition Classes
- GeneralizedLinearRegressionSummary
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- @Since("2.0.0")
- lazy val rank: Long
The numeric rank of the fitted linear model.
The numeric rank of the fitted linear model.
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- GeneralizedLinearRegressionSummary
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- lazy val residualDegreeOfFreedom: Long
The residual degrees of freedom.
The residual degrees of freedom.
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- GeneralizedLinearRegressionSummary
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- lazy val residualDegreeOfFreedomNull: Long
The residual degrees of freedom for the null model.
The residual degrees of freedom for the null model.
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- GeneralizedLinearRegressionSummary
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- @Since("2.0.0")
- def residuals(residualsType: String): DataFrame
Get the residuals of the fitted model by type.
Get the residuals of the fitted model by type.
- residualsType
The type of residuals which should be returned. Supported options: deviance, pearson, working and response.
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- GeneralizedLinearRegressionSummary
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- @Since("2.0.0")
- def residuals(): DataFrame
Get the default residuals (deviance residuals) of the fitted model.
Get the default residuals (deviance residuals) of the fitted model.
- Definition Classes
- GeneralizedLinearRegressionSummary
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- @Since("2.0.0")
- val solver: String
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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 the underlying
WeightedLeastSquaresusing the "normal" solver.If
GeneralizedLinearRegression.fitInterceptis set to true, then the last element returned corresponds to the intercept.- Annotations
- @Since("2.0.0")
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(Since version 9)