# Spark ML -- Multilayer Perceptron

## Usage

ml_multilayer_perceptron(x, response, features, layers, iter.max = 100, seed = sample(.Machine$integer.max, 1), 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.
- layers
- A numeric vector describing the layers -- each element in the vector
gives the size of a layer. For example,
`c(4, 5, 2)`

would imply three layers,
with an input (feature) layer of size 4, an intermediate layer of size 5, and an
output (class) layer of size 2.
- iter.max
- The maximum number of iterations to use.
- seed
- A random seed. Set this value if you need your results to be
reproducible across repeated calls.
- ml.options
- Optional arguments, used to affect the model generated. See
`ml_options`

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

## Description

Creates and trains multilayer perceptron on a Spark DataFrame.

## 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_logistic_regression`

,

`ml_naive_bayes`

,

`ml_one_vs_rest`

,

`ml_pca`

,

`ml_random_forest`

,

`ml_survival_regression`