ml_multilayer_perceptron
From sparklyr v0.3.14
by Javier Luraschi
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; 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
Community examples
Looks like there are no examples yet.