ml_generalized_linear_regression(x, response, features, intercept = TRUE, family = gaussian(link = "identity"), max.iter = 100L, ...)
tbl_spark
).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.glm
.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.
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