ml_pca
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_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_multilayer_perceptron,
ml_naive_bayes,
ml_one_vs_rest,
ml_random_forest,
ml_survival_regression