GeneralizedLinearRegressionSummary¶

class pyspark.ml.regression.GeneralizedLinearRegressionSummary(java_obj: Optional[JavaObject] = None)¶

Generalized linear regression results evaluated on a dataset.

Methods

residuals([residualsType])

Get the residuals of the fitted model by type.

Attributes

`aic`	Akaike’s “An Information Criterion”(AIC) for the fitted model.
`degreesOfFreedom`	Degrees of freedom.
`deviance`	The deviance for the fitted model.
`dispersion`	The dispersion of the fitted model.
`nullDeviance`	The deviance for the null model.
`numInstances`	Number of instances in DataFrame predictions.
`predictionCol`	Field in `predictions` which gives the predicted value of each instance.
`predictions`	Predictions output by the model’s transform method.
`rank`	The numeric rank of the fitted linear model.
`residualDegreeOfFreedom`	The residual degrees of freedom.
`residualDegreeOfFreedomNull`	The residual degrees of freedom for the null model.

Methods Documentation

residuals(residualsType: str = 'deviance') → pyspark.sql.dataframe.DataFrame¶

Get the residuals of the fitted model by type.

Parameters

residualsTypestr, optional: The type of residuals which should be returned. Supported options: deviance (default), pearson, working, and response.

Attributes Documentation

dispersion¶: 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.

predictionCol¶: Field in predictions which gives the predicted value of each instance. This is set to a new column name if the original model’s predictionCol is not set.

residualDegreeOfFreedomNull¶: The residual degrees of freedom for the null model.

GeneralizedLinearRegressionModel

GeneralizedLinearRegressionTrainingSummary