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. |