ml_pca

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Spark ML -- Principal Components Analysis

Perform principal components analysis on a Spark DataFrame.

Usage
ml_pca(x, features = dplyr::tbl_vars(x), ...)
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.
...
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

Aliases
  • ml_pca
Documentation reproduced from package sparklyr, version 0.3.0, License: file LICENSE

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