LinearSVC¶

In [ ]:

LinearSVC(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",
          class_weight: list = [1, 1],
          max_iter: int = 100)

Creates a LinearSVC object using the Vertica SVM (Support Vector Machine) algorithm. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples to each category, making it a non-probabilistic binary linear classifier.

Parameters¶

Name	Type	Optional	Description
name	str	❌	Name of the model to be stored in the DB.
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	✓	Whether to calculate the intercept for the model.
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.
class_weight	list	✓	Specifies how to determine weights of the two classes. It can be a list of 2 elements or one of the following method: auto : Weights each class according to the number of samples. none : No weights are used.
max_iter	int	✓	The maximum number of iterations that the algorithm performs.

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	float	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
classification_report	Computes a classification report using multiple metrics to evaluate the model (AUC, accuracy, PRC AUC, F1...). In case of multiclass classification, it will consider each category as positive and switch to the next one during the computation.
confusion_matrix	Computes the model's confusion matrix.
contour	Draws the model's contour plot.
cutoff_curve	Draws the model Cutoff curve.
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.
lift_chart	Draws the model Lift Chart.
plot	Draws the LinearSVC if the number of predictors is less than 3.
prc_curve	Draws the model precision-recall curve.
predict	Predicts using the input relation.
roc_curve	Draws the model ROC curve.
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 [8]:

from verticapy.learn.svm import LinearSVC
model = LinearSVC(name = "iris_svm",
                  tol = 1e-4, 
                  C = 1.0, 
                  fit_intercept = True,
                  intercept_scaling = 1.0,
                  intercept_mode = "regularized",
                  class_weight = [1, 1],
                  max_iter = 100)
display(model)

<LinearSVC>