ml_lda

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Spark ML -- Latent Dirichlet Allocation

Fit a Latent Dirichlet Allocation (LDA) model to a Spark DataFrame.

Usage
ml_lda(x, features = dplyr::tbl_vars(x), k = length(features), ...)
Arguments
x
An object coercable to a Spark DataFrame (typically, a tbl_spark).
features
The name of features (terms) to use for the model fit.
k
The number of topics to estimate.
...
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_linear_regression, ml_logistic_regression, ml_multilayer_perceptron, ml_naive_bayes, ml_one_vs_rest, ml_pca, ml_random_forest, ml_survival_regression

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

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