verticapy.machine_learning.memmodel.preprocessing.MinMaxScaler.transform_sql#
- MinMaxScaler.transform_sql(X: list | ndarray) list[str] #
Transforms and returns the SQL needed to deploy the
Scaler
.Parameters#
- X: ArrayLike
The names or values of the input predictors.
Returns#
- list
SQL code.
Examples#
Import the required module.
from verticapy.machine_learning.memmodel.preprocessing import StandardScaler
We will use the following attributes:
mean = [0.4, 0.1] std = [0.5, 0.2]
Let’s create a model.
model_sts = StandardScaler(mean, std)
Let’s use the following column names:
cnames = ['col1', 'col2']
Get the SQL code needed to deploy the model.
model_sts.transform_sql(cnames) Out[6]: ['(col1 - 0.4) / 0.5', '(col2 - 0.1) / 0.2']
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.