ml_multilayer_perceptron

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Spark ML -- Multilayer Perceptron

Creates and trains multilayer perceptron on a Spark DataFrame.

Usage
ml_multilayer_perceptron(x, response, features, layers, max.iter = 100, seed = sample(.Machine$integer.max, 1), ...)
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.
max.iter
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.
...
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

Aliases
  • ml_multilayer_perceptron
Documentation reproduced from package sparklyr, version 0.2.32, License: file LICENSE

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