Spark ML -- Generalized Linear Regression
Perform generalized linear regression on a Spark DataFrame.
ml_generalized_linear_regression(x, response, features, intercept = TRUE, family = gaussian(link = "identity"), weights.column = NULL, iter.max = 100L, ml.options = ml_options(), ...)
An object coercable to a Spark DataFrame (typically, a
The name of the response vector (as a length-one character
vector), or a formula, giving a symbolic description of the model to be
The name of features (terms) to use for the model fit.
Boolean; should the model be fit with an intercept term?
The family / link function to use; analogous to those normally
passed in to calls to R's own
The name of the column to use as weights for the model fit.
The maximum number of iterations to use.
Optional arguments, used to affect the model generated. See
Optional arguments. The
In contrast to
ml_logistic_regression(), these routines do not allow you to
tweak the loss function (e.g. for elastic net regression); however, the model
fits returned by this routine are generally richer in regards to information
provided for assessing the quality of fit.
Other Spark ML routines: