verticapy.machine_learning.memmodel.linear_model.LinearModel.predict_proba_sql#
- LinearModel.predict_proba_sql(X: list | ndarray) list[str] #
Returns the SQL code needed to deploy the model probabilities using its attributes.
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.linear_model import LinearModelClassifier
We will use the following attributes:
coefficients = [0.5, 1.2] intercept = 2.0
Let’s create a model.
model_lmc = LinearModelClassifier(coefficients, intercept)
Let’s use the following column names:
cnames = ['col1', 'col2']
Get the SQL code needed to deploy the model.
model_lmc.predict_proba_sql(cnames) Out[6]: ['1 - (1 / (1 + EXP(- (2.0 + 0.5 * col1 + 1.2 * col2))))', '1 / (1 + EXP(- (2.0 + 0.5 * col1 + 1.2 * col2)))']
Note
Refer to
LinearModelClassifier
for more information about the different methods and usages.