# ml_logistic_regression

##### Spark ML -- Logistic Regression

Perform logistic regression on a Spark DataFrame.

##### 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. The

`data`

argument can be used to specify the data to be used when`x`

is a formula; this allows calls of the form`ml_linear_regression(y ~ x, data = tbl)`

, and is especially useful in conjunction with`do`

.

##### 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`

*Documentation reproduced from package sparklyr, version 0.5.3, License: Apache License 2.0 | file LICENSE*