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_options
for more details.- ...
Optional arguments. The
data
argument can be used to specify the data to be used whenx
is 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