verticapy.machine_learning.memmodel.linear_model.LinearModel#

class verticapy.machine_learning.memmodel.linear_model.LinearModel(coef: list | ndarray, intercept: float = 0.0)#

InMemoryModel implementation of linear algorithms.

Parameters#

Note

memmodel() are defined entirely by their attributes. For example, coefficients and intercept define a linear regression model.

Attributes are identical to the input parameters, followed by an underscore (‘_’).

Initalization

Import the required module.

from verticapy.machine_learning.memmodel.linear_model import LinearModel

A linear model is defined by its coefficients and an intercept value. In this example, we will use the following:

coefficients = [0.5, 1.2]

intercept = 2.0

Let’s create a LinearModel.

model_lm = LinearModel(coefficients, intercept)

Create a dataset.

data = [[1.0, 0.3], [2.0, -0.6]]

Making In-Memory Predictions

Use predict() method to do predictions.

model_lm.predict(data)
Out[6]: array([2.86, 2.28])

Deploy SQL Code

Let’s use the following column names:

cnames = ['col1', 'col2']

Use predict_sql() method to get the SQL code needed to deploy the model using its attributes.

model_lm.predict_sql(cnames)
Out[8]: '2.0 + 0.5 * col1 + 1.2 * col2'

Hint

This object can be pickled and used in any in-memory environment, just like SKLEARN models.

Methods

`__init__`(coef[, intercept])
`get_attributes`()	Returns the model attributes.
`predict`(X)	Predicts using the input Matrix.
`predict_proba`(X)	Computes the model's probabilites using the input matrix.
`predict_proba_sql`(X)	Returns the SQL code needed to deploy the model probabilities using its attributes.
`predict_sql`(X)	Returns the SQL code needed to deploy the model using its attributes.
`set_attributes`(**kwargs)	Sets the model attributes.

Attributes

Must be overridden in child class