LinearSVR¶

In [ ]:

LinearSVR(name: str,
          cursor = None,
          tol: float = 1e-4, 
          C: float = 1.0, 
          fit_intercept: bool = True,
          intercept_scaling: float = 1.0,
          intercept_mode: str = "regularized",
          acceptable_error_margin: float = 0.1,
          max_iter: int = 100)

Creates a LinearSVR object using the Vertica SVM (Support Vector Machine) algorithm. This algorithm finds the hyperplane used to approximate distribution of the data.

Parameters¶

Name	Type	Optional	Description
name	str	❌	Name of the model to be stored in the database.
cursor	DBcursor	✓	Vertica DB cursor.
tol	float	✓	to use to control accuracy.
C	float	✓	The weight for misclassification cost. The algorithm minimizes the regularization cost and the misclassification cost.
fit_intercept	bool	✓	A bool to fit also the intercept.
intercept_scaling	float	✓	A float value, serves as the value of a dummy feature whose coefficient Vertica uses to calculate the model intercept. Because the dummy feature is not in the training data, its values are set to a constant, by default set to 1.
intercept_mode	str	✓	Specify how to treat the intercept. regularized : Fits the intercept and applies a regularization on it. unregularized : Fits the intercept but does not include it in regularization.
acceptable_error_margin	float	✓	Defines the acceptable error margin. Any data points outside this region add a penalty to the cost function.
max_iter	int	✓	The maximum number of iterations that the algorithm performs.

Attributes¶

After the object creation, all the parameters become attributes. The model will also create extra attributes 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 model features importance by normalizing the LinearSVC hyperplan coefficients.
fit	Trains the model.
get_attr	Returns the model attribute.
get_params	Returns the model Parameters.
plot	Draws the LinearSVR if the number of predictors is lesser than 2.
predict	Predicts using the input relation.
regression_report	Computes a regression report using multiple metrics to evaluate the model (r2, mse, max error...).
score	Computes the model score.
set_cursor	Sets a new database cursor.
set_params	Sets the parameters of the model.
shapExplainer	Creates a shapExplainer for 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_sklearn	Converts this Vertica model to an sklearn model.
to_sql	Returns the SQL code needed to deploy the model without using Vertica built-in functions.

Example¶

In [10]:

from verticapy.learn.svm import LinearSVR
model = LinearSVR(name = "winequality_svm",
                  tol = 1e-4, 
                  C = 1.0, 
                  fit_intercept = True,
                  intercept_scaling = 1.0,
                  intercept_mode = "regularized",
                  acceptable_error_margin = 0.1,
                  max_iter = 100)
display(model)

<LinearSVR>