pyspark.ml.regression.
GeneralizedLinearRegressionTrainingSummary
Generalized linear regression training results.
New in version 2.0.0.
Methods
residuals([residualsType])
residuals
Get the residuals of the fitted model by type.
Attributes
aic
Akaike’s “An Information Criterion”(AIC) for the fitted model.
coefficientStandardErrors
Standard error of estimated coefficients and intercept.
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.
numIterations
Number of training iterations.
pValues
Two-sided p-value of estimated coefficients and intercept.
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.
solver
The numeric solver used for training.
tValues
T-statistic of estimated coefficients and intercept.
Methods Documentation
The type of residuals which should be returned. Supported options: deviance (default), pearson, working, and response.
Attributes Documentation
If GeneralizedLinearRegression.fitIntercept is set to True, then the last element returned corresponds to the intercept.
GeneralizedLinearRegression.fitIntercept
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.
New in version 2.2.0.
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.