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(), ...)An object coercable to a Spark DataFrame (typically, a
tbl_spark).
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.
The name of features (terms) to use for the model fit.
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.
The maximum number of iterations to use.
A random seed. Set this value if you need your results to be reproducible across repeated calls.
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.
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