Spark ML -- Multilayer Perceptron

Creates and trains multilayer perceptron on a Spark DataFrame.

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. 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.

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