verticapy.machine_learning.vertica.preprocessing.MinMaxScaler.deployInverseSQL#
- MinMaxScaler.deployInverseSQL(key_columns: str | list[str] | None = None, exclude_columns: str | list[str] | None = None, X: str | list[str] | None = None) str #
Returns the SQL code needed to deploy the inverse model.
Parameters#
- key_columns: SQLColumns, optional
Predictors used during the algorithm computation which will be deployed with the principal components.
- exclude_columns: SQLColumns, optional
Columns to exclude from the prediction.
- X: SQLColumns, optional
list
of the columns used to deploy the inverse model. If empty, the model predictors are used.
Returns#
- str
the SQL code needed to deploy the inverse model.
Examples#
We import
verticapy
:import verticapy as vp
For this example, we will use a dummy dataset.
data = vp.vDataFrame( { "values": [1, 1.01, 1.02, 1.05, 1.024], } )
Let’s import the model:
from verticapy.machine_learning.vertica import Scaler
Then we can create the model:
model = Scaler(method = "zscore")
We can now fit the model:
model.fit(data)
To get the Model Vertica Inverse SQL, use below:
model.deployInverseSQL() Out[48]: 'REVERSE_NORMALIZE("values" USING PARAMETERS model_name = \'"public"."_verticapy_tmp_scaler_v_demo_6972d166e22d11eea3a80242ac120002_"\', match_by_pos = \'true\')'
Important
For this example, a specific model is utilized, and it may not correspond exactly to the model you are working with. To see a comprehensive example specific to your class of interest, please refer to that particular class.