ml_pca

From sparklyr v0.3.7 by Javier Luraschi

Spark ML -- Principal Components Analysis

Perform principal components analysis on a Spark DataFrame.

Usage

ml_pca(x, features = dplyr::tbl_vars(x), ml.options = ml_options(), ...)

Arguments

x: An object coercable to a Spark DataFrame (typically, a tbl_spark).
features: The columns to use in the principal components analysis. Defaults to all columns in x.
ml.options: Optional arguments, used to affect the model generated. See ml_options for 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_multilayer_perceptron, ml_naive_bayes, ml_one_vs_rest, ml_random_forest, ml_survival_regression

Documentation reproduced from package sparklyr, version 0.3.7, License: file LICENSE

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