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