ml_generalized_linear_regression {sparklyr} | R Documentation |
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(), ...)
x |
An object coercable to a Spark DataFrame (typically, a
|
response |
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 |
features |
The name of features (terms) to use for the model fit. |
intercept |
Boolean; should the model be fit with an intercept term? |
family |
The family / link function to use; analogous to those normally
passed in to calls to R's own |
weights.column |
The name of the column to use as weights for the model fit. |
iter.max |
The maximum number of iterations to use. |
ml.options |
Optional arguments, used to affect the model generated. See
|
... |
Optional arguments. The |
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