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
tbl_spark).
The name of the response vector (as a length-one character
vector), or a formula, giving a symbolic description of the model to be
fitted. When response is a formula, it is used in preference to other
parameters to set the response, features, and intercept
parameters (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 - 1 in 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 glm.
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
ml_options for more details.
Optional arguments. The data argument can be used to
specify the data to be used when x is a formula; this allows calls
of the form ml_linear_regression(y ~ x, data = tbl), and is
especially useful in conjunction with do.
In contrast to ml_linear_regression() and
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: ml_als_factorization,
ml_decision_tree,
ml_gradient_boosted_trees,
ml_kmeans, ml_lda,
ml_linear_regression,
ml_logistic_regression,
ml_multilayer_perceptron,
ml_naive_bayes,
ml_one_vs_rest, ml_pca,
ml_random_forest,
ml_survival_regression