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"), 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
responseis a formula, it is used in preference to other parameters to set the
interceptparameters (if available). Currently, only simple linear combinations of existing parameters is supposed; e.g.
response ~ feature1 + feature2 + .... The intercept term can be omitted by using
- 1in the model fit.
- 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 maximum number of iterations to use.
- Optional arguments, used to affect the model generated. See
ml_optionsfor more details.
- Optional arguments; currently unused.
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: