# Spark ML -- Logistic Regression

## Usage

ml_logistic_regression(x, response, features, intercept = TRUE, alpha = 0, lambda = 0, iter.max = 100L, ml.options = ml_options(), ...)

## Arguments

- x
- An object coercable to a Spark DataFrame (typically, a
`tbl_spark`

).
- 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
`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.
- 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.
- iter.max
- The maximum number of iterations to use.
- ml.options
- Optional arguments, used to affect the model generated. See
`ml_options`

for more details.
- ...
- Optional arguments; currently unused.

## Description

Perform logistic regression on a Spark DataFrame.

## 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`