ml_linear_regression(x, response, features, intercept = TRUE, alpha = 0, lambda = 0, iter.max = 100L, ml.options = ml_options(), ...)
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.ml_options
for more details.In particular, with alpha
set to 1, the parameterization
is equivalent to a lasso
model; if alpha
is set to 0, the parameterization is equivalent to
a ridge regression model.
ml_als_factorization
,
ml_decision_tree
,
ml_generalized_linear_regression
,
ml_gradient_boosted_trees
,
ml_kmeans
, ml_lda
,
ml_logistic_regression
,
ml_multilayer_perceptron
,
ml_naive_bayes
,
ml_one_vs_rest
, ml_pca
,
ml_random_forest
,
ml_survival_regression