Tidying methods for Spark ML linear models
These methods summarize the results of Spark ML models into tidy forms.
## S3 method for class 'ml_model_generalized_linear_regression'
tidy(x, exponentiate = FALSE, ...)
## S3 method for class 'ml_model_linear_regression'
tidy(x, ...)
## S3 method for class 'ml_model_generalized_linear_regression'
augment(
x,
newdata = NULL,
type.residuals = c("working", "deviance", "pearson", "response"),
...
)
## S3 method for class 'ml_model_linear_regression'
augment(
x,
newdata = NULL,
type.residuals = c("working", "deviance", "pearson", "response"),
...
)
## S3 method for class 'ml_model_generalized_linear_regression'
glance(x, ...)
## S3 method for class 'ml_model_linear_regression'
glance(x, ...)x |
a Spark ML model. |
exponentiate |
For GLM, whether to exponentiate the coefficient estimates (typical for logistic regression.) |
... |
extra arguments (not used.) |
newdata |
a tbl_spark of new data to use for prediction. |
type.residuals |
type of residuals, defaults to |
The residuals attached by augment are of type "working" by default,
which is different from the default of "deviance" for residuals() or sdf_residuals().
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