ElasticNet¶

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ElasticNet(name: str,
           tol: float = 1e-6, 
           C: float = 1.0, 
           max_iter: int = 100, 
           solver: str = 'CGD', 
           l1_ratio: float = 0.5,
           fit_intercept: bool = True)

Creates a ElasticNet object by using the Vertica linear regression function on the data. Elastic net regularization is a regularized regression method that linearly combines the L1 and L2 penalties of the Lasso and Ridge methods.

Parameters¶

Name	Type	Optional	Description
name	str	❌	Name of the model to be stored in the database.
tol	float	✓	Determines whether the algorithm has reached the specified accuracy result.
C	float	✓	The regularization parameter value. The value must be zero or non-negative.
max_iter	int	✓	Determines the maximum number of iterations the algorithm performs before achieving the specified accuracy result.
solver	str	✓	The optimizer method to use to train the model. Newton : Newton Method BFGS : Broyden Fletcher Goldfarb Shanno CGD : Coordinate Gradient Descent
l1_ratio	float	✓	ENet mixture parameter that defines how much L1 versus L2 regularization to provide.
fit_intercept	bool	✓	Boolean, specifies whether the model includes an intercept. If False, intercepts are not used when training the model. fit_intercept should not be False when using the BFGS optimizer.

Attributes¶

After the object is created, all parameters become attributes. Additional attributes will be created when fitting the model:

Name	Type	Description
coef_	tablesample	Coefficients and their mathematical information (pvalue, std, value...)
input_relation	str	Training relation.
X	list	List of the predictors.
y	str	Response column.
test_relation	str	Relation to use to test the model. All model methods are abstractions that simplify the process. The testing relation will be used by the methods to evaluate the model. If empty, the training relation will be used instead. This attribute can be changed at any time.

Methods¶

Name	Description
contour	Draws the model's contour plot.
deploySQL	Returns the SQL code needed to deploy the model.
drop	Drops the model from the Vertica DB.
features_importance	Computes the importance of model features using the Gini Index.
fit	Trains the model.
get_attr	Returns the model attribute.
get_params	Returns the model parameters.
plot	Draws the Linear Regression if the number of predictors is equal to 1 or 2.
predict	Predicts using the input relation.
regression_report / report	Computes a regression report using multiple metrics to evaluate the model (r2, mse, max error...).
score	Computes the model score.
set_params	Sets the parameters of the model.
to_memmodel	Converts a specified Vertica model to a memModel model.
to_python	Returns the Python code needed to deploy the model without using built-in Vertica functions.
to_sql	Returns the SQL code needed to deploy the model without using Vertica built-in functions.

Example¶

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from verticapy.learn.linear_model import ElasticNet
model = ElasticNet(name = "public.LR_winequality",
                   tol = 1e-4, 
                   C = 1.0, 
                   max_iter = 100, 
                   solver = 'CGD', 
                   l1_ratio = 0.5)
display(model)

<LinearRegression>