Model.deploySQL¶

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Model.deploySQL(n_components: int = 0,
                cutoff: float = 1,
                key_columns: list = [],
                exclude_columns: list = [],
                X: list = [])

Returns the SQL statement needed to deploy the model.

Parameters¶

Name	Type	Optional	Description
n_components	int	✓	Number of components to return. If set to 0, all the components will be deployed.
cutoff	float	✓	Specifies the minimum accumulated explained variance. Components are taken until the accumulated explained variance reaches this value.
key_columns	list	✓	Predictors used during the algorithm computation which will be deployed with the principal components.
exclude_columns	list	✓	Columns to exclude from the prediction.
X	list	✓	List of the columns used to deploy the model. If empty, the model predictors will be used.

Returns¶

str : the SQL statement needed to deploy the model.

Example¶

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from verticapy.learn.decomposition import PCA
model = PCA(name = "public.pca_iris")
model.fit("public.iris", ["PetalLengthCm", "SepalLengthCm", "SepalWidthCm"])
display(model.deploySQL())

APPLY_PCA("PetalLengthCm", "SepalLengthCm", "SepalWidthCm" USING PARAMETERS model_name = 'public.pca_iris', match_by_pos = 'true', cutoff = 1)