ml_multilayer_perceptron
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
responseis a formula, it is used in preference to other parameters to set theresponse,features, andinterceptparameters (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- 1in 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_optionsfor more details.- ...
Optional arguments. The
dataargument can be used to specify the data to be used whenxis a formula; this allows calls of the formml_linear_regression(y ~ x, data = tbl), and is especially useful in conjunction withdo.
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