GeneralizedLinearRegressionTrainingSummary#

class pyspark.ml.regression.GeneralizedLinearRegressionTrainingSummary(java_obj=None)[source]#

Generalized linear regression training results.

Added in version 2.0.0.

Methods

residuals([residualsType])

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

residuals(residualsType='deviance')#

Get the residuals of the fitted model by type.

Added in version 2.0.0.

Parameters:

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

Attributes Documentation

aic#: Akaike’s “An Information Criterion”(AIC) for the fitted model.

Added in version 2.0.0.

coefficientStandardErrors#

Standard error of estimated coefficients and intercept.

If GeneralizedLinearRegression.fitIntercept is set to True, then the last element returned corresponds to the intercept.

Added in version 2.0.0.

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.

Added in version 2.0.0.

numInstances#: Number of instances in DataFrame predictions.

Added in version 2.2.0.

pValues#

Two-sided p-value of estimated coefficients and intercept.

If GeneralizedLinearRegression.fitIntercept is set to True, then the last element returned corresponds to the intercept.

Added in version 2.0.0.

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.

Added in version 2.0.0.

predictions#: Predictions output by the model’s transform method.

Added in version 2.0.0.

rank#: The numeric rank of the fitted linear model.

Added in version 2.0.0.

residualDegreeOfFreedom#: The residual degrees of freedom.

Added in version 2.0.0.

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

Added in version 2.0.0.

tValues#

T-statistic of estimated coefficients and intercept.

If GeneralizedLinearRegression.fitIntercept is set to True, then the last element returned corresponds to the intercept.

Added in version 2.0.0.