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Matrix Decomposition models. This module is styled after Scikit-Learn's decomposition module: https://scikit-learn.org/stable/modules/decomposition.html .
Classes
PCA
PCA
(
n_components
:
typing
.
Optional
[
typing
.
Union
[
int
,
float
]]
=
None
,
*
,
svd_solver
:
typing
.
Literal
[
"full"
,
"randomized"
,
"auto"
]
=
"auto"
)
Principal component analysis (PCA).
Parameters
Name
Description
n_components
int, float or None, default None
Number of components to keep. If n_components is not set, all components are kept, n_components = min(n_samples, n_features). If 0 < n_components < 1, select the number of components such that the amount of variance that needs to be explained is greater than the percentage specified by n_components.
svd_solver
"full", "randomized" or "auto", default "auto"
The solver to use to calculate the principal components. Details: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-pca#pca_solver .
