# ml_multilayer_perceptron

From sparklyr v0.3.11
by Javier Luraschi

##### Spark ML -- Multilayer Perceptron

Creates and trains multilayer perceptron on a Spark DataFrame.

##### 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.

##### 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`

*Documentation reproduced from package sparklyr, version 0.3.11, License: file LICENSE*

### Community examples

Looks like there are no examples yet.