ml_multilayer_perceptron {sparklyr} | R Documentation |
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(), ...)
x |
An object coercable to a Spark DataFrame (typically, a
|
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 |
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, |
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
|
... |
Optional arguments. The |
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