Spark ML  Logistic Regression
Perform logistic regression on a Spark DataFrame.
ml_logistic_regression(x, response, features, intercept = TRUE, alpha = 0,
lambda = 0, weights.column = NULL, iter.max = 100L,
ml.options = ml_options(), ...)
Arguments
x  An object coercable to a Spark DataFrame (typically, a

response  The name of the response vector (as a lengthone 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? 
alpha, lambda  Parameters controlling loss function penalization (for e.g. lasso, elastic net, and ridge regression). See Details for more information. 
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 
Details
Spark implements for both \(L1\) and \(L2\) regularization in linear regression models. See the preamble in the Spark Classification and Regression documentation for more details on how the loss function is parameterized.
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.
See also
Other Spark ML routines: ml_als_factorization
,
ml_decision_tree
,
ml_generalized_linear_regression
,
ml_gradient_boosted_trees
,
ml_kmeans
, ml_lda
,
ml_linear_regression
,
ml_multilayer_perceptron
,
ml_naive_bayes
,
ml_one_vs_rest
, ml_pca
,
ml_random_forest
,
ml_survival_regression