XGBoostRegressor¶

In [ ]:

XGBoostRegressor(name: str,
                 cursor=None,
                 max_ntree: int = 10,
                 max_depth: int = 5,
                 nbins: int = 32,
                 objective: str = 'squarederror',
                 split_proposal_method: str = 'global',
                 tol: float = 0.001,
                 learning_rate: float = 0.1,
                 min_split_loss: float = 0,
                 weight_reg: float = 0,
                 sample: float = 1,)

Creates a XGBoostRegressor object by using the Vertica XGBoost algorithm on the data.

Parameters¶

Name	Type	Optional	Description
name	str	❌	Name of the model to be stored in the database.
cursor	DBcursor	✓	Vertica DB cursor.
max_ntree	int	✓	Maximum number of trees that will be created.
max_depth	int	✓	Maximum depth of each tree.
nbins	int	✓	Number of bins to use for finding splits in each column, more splits leads to longer runtime but more fine-grained and possibly better splits.
objective	str	✓	The objective/loss function that will be used to iteratively improve the model.
max_depth	int	✓	The maximum depth for growing each tree, an integer between 1 and 100, inclusive.
split_proposal_method	str	✓	approximate splitting strategy. Can be 'global' or 'local' (not yet supported).
tol	float	✓	approximation error of quantile summary structures used in the approximate split finding method.
learning_rate	float	✓	weight applied to each tree's prediction, reduces each tree's impact allowing for later trees to contribute, keeping earlier trees from 'hogging' all the improvements.
min_split_loss	float	✓	Each split must improve the objective function value of the model by at least this much in order to not be pruned. Value of 0 is the same as turning off this parameter (trees will still be pruned based on positive/negative objective function values).
weight_reg	float	✓	Regularization term that is applied to the weights of the leaves in the regression tree. The higher this value is, the more sparse/smooth the weights will be, which often helps prevent overfitting.
sample	float	✓	Fraction of rows to use in training per iteration.

Attributes¶

After the object creation, all the parameters become attributes. The model will also create extra attributes when fitting the model:

Name	Type	Description
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
deploySQL	Returns the SQL code needed to deploy the model.
drop	Drops the model from the Vertica DB.
export_graphviz	Converts the input tree to graphviz.
features_importance	Computes the model features importance using the Gini Index.
fit	Trains the model.
get_attr	Returns the model attribute.
get_params	Returns the model Parameters.
get_tree	Returns a tablesample with all the input tree information.
plot	Draws the Model.
plot_tree	Draws the input tree. The module anytree must be installed in the machine.
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 DB cursor.
set_params	Sets the parameters of the model.
shapExplainer	Creates a shapExplainer for the model.
to_sklearn	Converts the Vertica model to an sklearn model.

Example¶

In [10]:

from verticapy.learn.ensemble import XGBoostRegressor
model = XGBoostRegressor(name = "public.xgb_titanic",
                         max_ntree = 20,
                         max_depth = 15,)
display(model)

<XGBoostRegressor>