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Regression

---

Linear Models

Linear Regression

linear_model.LinearRegression([name, ...])

Creates a LinearRegression object using the Vertica Linear Regression algorithm.

Methods:

LinearRegression.contour([nbins, chart])	Draws the model's contour plot.
LinearRegression.deploySQL([X])	Returns the SQL code needed to deploy the model.
LinearRegression.does_model_exists(name[, ...])	Checks whether the model is stored in the Vertica database.
LinearRegression.drop()	Drops the model from the Vertica database.
LinearRegression.export_models(name, path[, ...])	Exports machine learning models.
LinearRegression.features_importance([show, ...])	Computes the model's features importance.
LinearRegression.fit(input_relation, X, y[, ...])	Trains the model.
LinearRegression.get_attributes([attr_name])	Returns the model attributes.
LinearRegression.get_match_index(x, col_list)	Returns the matching index.
LinearRegression.get_params()	Returns the parameters of the model.
LinearRegression.get_plotting_lib([...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
LinearRegression.get_vertica_attributes([...])	Returns the model Vertica attributes.
LinearRegression.import_models(path[, ...])	Imports machine learning models.
LinearRegression.plot([max_nb_points, chart])	Draws the model.
LinearRegression.predict(vdf[, X, name, inplace])	Predicts using the input relation.
LinearRegression.register(registered_name[, ...])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
LinearRegression.regression_report([metrics])	Computes a regression report
LinearRegression.report([metrics])	Computes a regression report
LinearRegression.score([metric])	Computes the model score.
LinearRegression.set_params([parameters])	Sets the parameters of the model.
LinearRegression.summarize()	Summarizes the model.
LinearRegression.to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
LinearRegression.to_pmml(path)	Exports the model to PMML.
LinearRegression.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
LinearRegression.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
LinearRegression.to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

LinearRegression.object_type

Ridge

linear_model.Ridge([name, overwrite_model, ...])

Creates a Ridge object using the Vertica Linear Regression algorithm.

Methods:

Ridge.contour([nbins, chart])	Draws the model's contour plot.
Ridge.deploySQL([X])	Returns the SQL code needed to deploy the model.
Ridge.does_model_exists(name[, raise_error, ...])	Checks whether the model is stored in the Vertica database.
Ridge.drop()	Drops the model from the Vertica database.
Ridge.export_models(name, path[, kind])	Exports machine learning models.
Ridge.features_importance([show, chart])	Computes the model's features importance.
Ridge.fit(input_relation, X, y[, ...])	Trains the model.
Ridge.get_attributes([attr_name])	Returns the model attributes.
Ridge.get_match_index(x, col_list[, str_check])	Returns the matching index.
Ridge.get_params()	Returns the parameters of the model.
Ridge.get_plotting_lib([class_name, chart, ...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
Ridge.get_vertica_attributes([attr_name])	Returns the model Vertica attributes.
Ridge.import_models(path[, schema, kind])	Imports machine learning models.
Ridge.plot([max_nb_points, chart])	Draws the model.
Ridge.predict(vdf[, X, name, inplace])	Predicts using the input relation.
Ridge.register(registered_name[, raise_error])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
Ridge.regression_report([metrics])	Computes a regression report
Ridge.report([metrics])	Computes a regression report
Ridge.score([metric])	Computes the model score.
Ridge.set_params([parameters])	Sets the parameters of the model.
Ridge.summarize()	Summarizes the model.
Ridge.to_binary(path)	Exports the model to the Vertica Binary format.
Ridge.to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
Ridge.to_pmml(path)	Exports the model to PMML.
Ridge.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
Ridge.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
Ridge.to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

Ridge.object_type

Lasso

linear_model.Lasso([name, overwrite_model, ...])

Creates a Lasso object using the Vertica Linear Regression algorithm.

Methods:

Lasso.contour([nbins, chart])	Draws the model's contour plot.
Lasso.deploySQL([X])	Returns the SQL code needed to deploy the model.
Lasso.does_model_exists(name[, raise_error, ...])	Checks whether the model is stored in the Vertica database.
Lasso.drop()	Drops the model from the Vertica database.
Lasso.export_models(name, path[, kind])	Exports machine learning models.
Lasso.features_importance([show, chart])	Computes the model's features importance.
Lasso.fit(input_relation, X, y[, ...])	Trains the model.
Lasso.get_attributes([attr_name])	Returns the model attributes.
Lasso.get_match_index(x, col_list[, str_check])	Returns the matching index.
Lasso.get_params()	Returns the parameters of the model.
Lasso.get_plotting_lib([class_name, chart, ...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
Lasso.get_vertica_attributes([attr_name])	Returns the model Vertica attributes.
Lasso.import_models(path[, schema, kind])	Imports machine learning models.
Lasso.plot([max_nb_points, chart])	Draws the model.
Lasso.predict(vdf[, X, name, inplace])	Predicts using the input relation.
Lasso.register(registered_name[, raise_error])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
Lasso.regression_report([metrics])	Computes a regression report
Lasso.report([metrics])	Computes a regression report
Lasso.score([metric])	Computes the model score.
Lasso.set_params([parameters])	Sets the parameters of the model.
Lasso.summarize()	Summarizes the model.
Lasso.to_binary(path)	Exports the model to the Vertica Binary format.
Lasso.to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
Lasso.to_pmml(path)	Exports the model to PMML.
Lasso.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
Lasso.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
Lasso.to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

Lasso.object_type

Elastic Net

linear_model.ElasticNet([name, ...])

Creates an ElasticNet object using the Vertica Linear Regression algorithm.

Methods:

ElasticNet.contour([nbins, chart])	Draws the model's contour plot.
ElasticNet.deploySQL([X])	Returns the SQL code needed to deploy the model.
ElasticNet.does_model_exists(name[, ...])	Checks whether the model is stored in the Vertica database.
ElasticNet.drop()	Drops the model from the Vertica database.
ElasticNet.export_models(name, path[, kind])	Exports machine learning models.
ElasticNet.features_importance([show, chart])	Computes the model's features importance.
ElasticNet.fit(input_relation, X, y[, ...])	Trains the model.
ElasticNet.get_attributes([attr_name])	Returns the model attributes.
ElasticNet.get_match_index(x, col_list[, ...])	Returns the matching index.
ElasticNet.get_params()	Returns the parameters of the model.
ElasticNet.get_plotting_lib([class_name, ...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
ElasticNet.get_vertica_attributes([attr_name])	Returns the model Vertica attributes.
ElasticNet.import_models(path[, schema, kind])	Imports machine learning models.
ElasticNet.plot([max_nb_points, chart])	Draws the model.
ElasticNet.predict(vdf[, X, name, inplace])	Predicts using the input relation.
ElasticNet.register(registered_name[, ...])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
ElasticNet.regression_report([metrics])	Computes a regression report
ElasticNet.report([metrics])	Computes a regression report
ElasticNet.score([metric])	Computes the model score.
ElasticNet.set_params([parameters])	Sets the parameters of the model.
ElasticNet.summarize()	Summarizes the model.
ElasticNet.to_binary(path)	Exports the model to the Vertica Binary format.
ElasticNet.to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
ElasticNet.to_pmml(path)	Exports the model to PMML.
ElasticNet.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
ElasticNet.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
ElasticNet.to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

ElasticNet.object_type

Linear SVR

svm.LinearSVR([name, overwrite_model, tol, ...])

Creates a LinearSVR object using the Vertica SVM (Support Vector Machine) algorithm.

Methods:

LinearSVR.contour([nbins, chart])	Draws the model's contour plot.
LinearSVR.deploySQL([X])	Returns the SQL code needed to deploy the model.
LinearSVR.does_model_exists(name[, ...])	Checks whether the model is stored in the Vertica database.
LinearSVR.drop()	Drops the model from the Vertica database.
LinearSVR.export_models(name, path[, kind])	Exports machine learning models.
LinearSVR.features_importance([show, chart])	Computes the model's features importance.
LinearSVR.fit(input_relation, X, y[, ...])	Trains the model.
LinearSVR.get_attributes([attr_name])	Returns the model attributes.
LinearSVR.get_match_index(x, col_list[, ...])	Returns the matching index.
LinearSVR.get_params()	Returns the parameters of the model.
LinearSVR.get_plotting_lib([class_name, ...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
LinearSVR.get_vertica_attributes([attr_name])	Returns the model Vertica attributes.
LinearSVR.import_models(path[, schema, kind])	Imports machine learning models.
LinearSVR.plot([max_nb_points, chart])	Draws the model.
LinearSVR.predict(vdf[, X, name, inplace])	Predicts using the input relation.
LinearSVR.register(registered_name[, ...])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
LinearSVR.regression_report([metrics])	Computes a regression report
LinearSVR.report([metrics])	Computes a regression report
LinearSVR.score([metric])	Computes the model score.
LinearSVR.set_params([parameters])	Sets the parameters of the model.
LinearSVR.summarize()	Summarizes the model.
LinearSVR.to_binary(path)	Exports the model to the Vertica Binary format.
LinearSVR.to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
LinearSVR.to_pmml(path)	Exports the model to PMML.
LinearSVR.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
LinearSVR.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
LinearSVR.to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

LinearSVR.object_type

Poisson Regression

linear_model.PoissonRegressor([name, ...])

Creates an PoissonRegressor object using the Vertica Poisson Regression algorithm.

Methods:

PoissonRegressor.contour([nbins, chart])	Draws the model's contour plot.
PoissonRegressor.deploySQL([X])	Returns the SQL code needed to deploy the model.
PoissonRegressor.does_model_exists(name[, ...])	Checks whether the model is stored in the Vertica database.
PoissonRegressor.drop()	Drops the model from the Vertica database.
PoissonRegressor.export_models(name, path[, ...])	Exports machine learning models.
PoissonRegressor.features_importance([show, ...])	Computes the model's features importance.
PoissonRegressor.fit(input_relation, X, y[, ...])	Trains the model.
PoissonRegressor.get_attributes([attr_name])	Returns the model attributes.
PoissonRegressor.get_match_index(x, col_list)	Returns the matching index.
PoissonRegressor.get_params()	Returns the parameters of the model.
PoissonRegressor.get_plotting_lib([...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
PoissonRegressor.get_vertica_attributes([...])	Returns the model Vertica attributes.
PoissonRegressor.import_models(path[, ...])	Imports machine learning models.
PoissonRegressor.plot([max_nb_points, chart])	Draws the model.
PoissonRegressor.predict(vdf[, X, name, inplace])	Predicts using the input relation.
PoissonRegressor.register(registered_name[, ...])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
PoissonRegressor.regression_report([metrics])	Computes a regression report
PoissonRegressor.report([metrics])	Computes a regression report
PoissonRegressor.score([metric])	Computes the model score.
PoissonRegressor.set_params([parameters])	Sets the parameters of the model.
PoissonRegressor.summarize()	Summarizes the model.
PoissonRegressor.to_binary(path)	Exports the model to the Vertica Binary format.
PoissonRegressor.to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
PoissonRegressor.to_pmml(path)	Exports the model to PMML.
PoissonRegressor.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
PoissonRegressor.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
PoissonRegressor.to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).

---

Tree-based Models

Dummy Tree

tree.DummyTreeRegressor([name, overwrite_model])

A regressor that overfits the training data.

Methods:

DummyTreeRegressor.contour([nbins, chart])	Draws the model's contour plot.
DummyTreeRegressor.deploySQL([X])	Returns the SQL code needed to deploy the model.
DummyTreeRegressor.does_model_exists(name[, ...])	Checks whether the model is stored in the Vertica database.
DummyTreeRegressor.drop()	Drops the model from the Vertica database.
DummyTreeRegressor.export_models(name, path)	Exports machine learning models.
DummyTreeRegressor.features_importance([...])	Computes the model's features importance.
DummyTreeRegressor.fit(input_relation, X, y)	Trains the model.
DummyTreeRegressor.get_attributes([attr_name])	Returns the model attributes.
DummyTreeRegressor.get_match_index(x, col_list)	Returns the matching index.
DummyTreeRegressor.get_params()	Returns the parameters of the model.
DummyTreeRegressor.get_plotting_lib([...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
DummyTreeRegressor.get_score([tree_id])	Returns the feature importance metrics for the input tree.
DummyTreeRegressor.get_tree([tree_id])	Returns a table with all the input tree information.
DummyTreeRegressor.get_vertica_attributes([...])	Returns the model Vertica attributes.
DummyTreeRegressor.import_models(path[, ...])	Imports machine learning models.
DummyTreeRegressor.plot([max_nb_points, chart])	Draws the model.
DummyTreeRegressor.plot_tree([tree_id, pic_path])	Draws the input tree.
DummyTreeRegressor.predict(vdf[, X, name, ...])	Predicts using the input relation.
DummyTreeRegressor.register(registered_name)	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
DummyTreeRegressor.regression_report([metrics])	Computes a regression report
DummyTreeRegressor.report([metrics])	Computes a regression report
DummyTreeRegressor.score([metric])	Computes the model score.
DummyTreeRegressor.set_params([parameters])	Sets the parameters of the model.
DummyTreeRegressor.summarize()	Summarizes the model.
DummyTreeRegressor.to_binary(path)	Exports the model to the Vertica Binary format.
DummyTreeRegressor.to_graphviz([tree_id, ...])	Returns the code for a Graphviz tree.
DummyTreeRegressor.to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
DummyTreeRegressor.to_pmml(path)	Exports the model to PMML.
DummyTreeRegressor.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
DummyTreeRegressor.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
DummyTreeRegressor.to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

DummyTreeRegressor.object_type

Decision Tree Regressor

tree.DecisionTreeRegressor([name, ...])

A DecisionTreeRegressor consisting of a single tree.

Methods:

DecisionTreeRegressor.contour([nbins, chart])	Draws the model's contour plot.
DecisionTreeRegressor.deploySQL([X])	Returns the SQL code needed to deploy the model.
DecisionTreeRegressor.does_model_exists(name)	Checks whether the model is stored in the Vertica database.
DecisionTreeRegressor.drop()	Drops the model from the Vertica database.
DecisionTreeRegressor.export_models(name, path)	Exports machine learning models.
DecisionTreeRegressor.features_importance([...])	Computes the model's features importance.
DecisionTreeRegressor.fit(input_relation, X, y)	Trains the model.
DecisionTreeRegressor.get_attributes([attr_name])	Returns the model attributes.
DecisionTreeRegressor.get_match_index(x, ...)	Returns the matching index.
DecisionTreeRegressor.get_params()	Returns the parameters of the model.
DecisionTreeRegressor.get_plotting_lib([...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
DecisionTreeRegressor.get_score([tree_id])	Returns the feature importance metrics for the input tree.
DecisionTreeRegressor.get_tree([tree_id])	Returns a table with all the input tree information.
DecisionTreeRegressor.get_vertica_attributes([...])	Returns the model Vertica attributes.
DecisionTreeRegressor.import_models(path[, ...])	Imports machine learning models.
DecisionTreeRegressor.plot([max_nb_points, ...])	Draws the model.
DecisionTreeRegressor.plot_tree([tree_id, ...])	Draws the input tree.
DecisionTreeRegressor.predict(vdf[, X, ...])	Predicts using the input relation.
DecisionTreeRegressor.register(registered_name)	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
DecisionTreeRegressor.regression_report([...])	Computes a regression report
DecisionTreeRegressor.report([metrics])	Computes a regression report
DecisionTreeRegressor.score([metric])	Computes the model score.
DecisionTreeRegressor.set_params([parameters])	Sets the parameters of the model.
DecisionTreeRegressor.summarize()	Summarizes the model.
DecisionTreeRegressor.to_binary(path)	Exports the model to the Vertica Binary format.
DecisionTreeRegressor.to_graphviz([tree_id, ...])	Returns the code for a Graphviz tree.
DecisionTreeRegressor.to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
DecisionTreeRegressor.to_pmml(path)	Exports the model to PMML.
DecisionTreeRegressor.to_python([...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
DecisionTreeRegressor.to_sql([X, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
DecisionTreeRegressor.to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

DecisionTreeRegressor.object_type

Random Forest Regressor

ensemble.RandomForestRegressor([name, ...])

Creates a RandomForestRegressor object using the Vertica RF_REGRESSOR function.

Methods:

RandomForestRegressor.contour([nbins, chart])	Draws the model's contour plot.
RandomForestRegressor.deploySQL([X])	Returns the SQL code needed to deploy the model.
RandomForestRegressor.does_model_exists(name)	Checks whether the model is stored in the Vertica database.
RandomForestRegressor.drop()	Drops the model from the Vertica database.
RandomForestRegressor.export_models(name, path)	Exports machine learning models.
RandomForestRegressor.features_importance([...])	Computes the model's features importance.
RandomForestRegressor.fit(input_relation, X, y)	Trains the model.
RandomForestRegressor.get_attributes([attr_name])	Returns the model attributes.
RandomForestRegressor.get_match_index(x, ...)	Returns the matching index.
RandomForestRegressor.get_params()	Returns the parameters of the model.
RandomForestRegressor.get_plotting_lib([...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
RandomForestRegressor.get_score([tree_id])	Returns the feature importance metrics for the input tree.
RandomForestRegressor.get_tree([tree_id])	Returns a table with all the input tree information.
RandomForestRegressor.get_vertica_attributes([...])	Returns the model Vertica attributes.
RandomForestRegressor.import_models(path[, ...])	Imports machine learning models.
RandomForestRegressor.plot([max_nb_points, ...])	Draws the model.
RandomForestRegressor.plot_tree([tree_id, ...])	Draws the input tree.
RandomForestRegressor.predict(vdf[, X, ...])	Predicts using the input relation.
RandomForestRegressor.register(registered_name)	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
RandomForestRegressor.regression_report([...])	Computes a regression report
RandomForestRegressor.report([metrics])	Computes a regression report
RandomForestRegressor.score([metric])	Computes the model score.
RandomForestRegressor.set_params([parameters])	Sets the parameters of the model.
RandomForestRegressor.summarize()	Summarizes the model.
RandomForestRegressor.to_binary(path)	Exports the model to the Vertica Binary format.
RandomForestRegressor.to_graphviz([tree_id, ...])	Returns the code for a Graphviz tree.
RandomForestRegressor.to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
RandomForestRegressor.to_pmml(path)	Exports the model to PMML.
RandomForestRegressor.to_python([...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
RandomForestRegressor.to_sql([X, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
RandomForestRegressor.to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

RandomForestRegressor.object_type

XGB Regressor

ensemble.XGBRegressor([name, ...])

Creates an XGBRegressor object using the Vertica XGB_REGRESSOR algorithm.

Methods:

XGBRegressor.contour([nbins, chart])	Draws the model's contour plot.
XGBRegressor.deploySQL([X])	Returns the SQL code needed to deploy the model.
XGBRegressor.does_model_exists(name[, ...])	Checks whether the model is stored in the Vertica database.
XGBRegressor.drop()	Drops the model from the Vertica database.
XGBRegressor.export_models(name, path[, kind])	Exports machine learning models.
XGBRegressor.features_importance([tree_id, ...])	Computes the model's features importance.
XGBRegressor.fit(input_relation, X, y[, ...])	Trains the model.
XGBRegressor.get_attributes([attr_name])	Returns the model attributes.
XGBRegressor.get_match_index(x, col_list[, ...])	Returns the matching index.
XGBRegressor.get_params()	Returns the parameters of the model.
XGBRegressor.get_plotting_lib([class_name, ...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
XGBRegressor.get_score([tree_id])	Returns the feature importance metrics for the input tree.
XGBRegressor.get_tree([tree_id])	Returns a table with all the input tree information.
XGBRegressor.get_vertica_attributes([attr_name])	Returns the model Vertica attributes.
XGBRegressor.import_models(path[, schema, kind])	Imports machine learning models.
XGBRegressor.plot([max_nb_points, chart])	Draws the model.
XGBRegressor.plot_tree([tree_id, pic_path])	Draws the input tree.
XGBRegressor.predict(vdf[, X, name, inplace])	Predicts using the input relation.
XGBRegressor.register(registered_name[, ...])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
XGBRegressor.regression_report([metrics])	Computes a regression report
XGBRegressor.report([metrics])	Computes a regression report
XGBRegressor.score([metric])	Computes the model score.
XGBRegressor.set_params([parameters])	Sets the parameters of the model.
XGBRegressor.summarize()	Summarizes the model.
XGBRegressor.to_binary(path)	Exports the model to the Vertica Binary format.
XGBRegressor.to_graphviz([tree_id, ...])	Returns the code for a Graphviz tree.
XGBRegressor.to_json([path])	Creates a Python XGBoost JSON file
XGBRegressor.to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
XGBRegressor.to_pmml(path)	Exports the model to PMML.
XGBRegressor.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
XGBRegressor.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
XGBRegressor.to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

XGBRegressor.object_type

---

Neighbors

K-Nearest Neighbors Regressor (Beta)

neighbors.KNeighborsRegressor([name, ...])

[Beta Version] Creates a KNeighborsRegressor object using the k-nearest neighbors algorithm.

Methods:

KNeighborsRegressor.contour([nbins, chart])	Draws the model's contour plot.
KNeighborsRegressor.deploySQL([X, ...])	Returns the SQL code needed to deploy the model.
KNeighborsRegressor.does_model_exists(name)	Checks whether the model is stored in the Vertica database.
KNeighborsRegressor.drop()	KNeighborsRegressor models are not stored in the Vertica DB.
KNeighborsRegressor.export_models(name, path)	Exports machine learning models.
KNeighborsRegressor.fit(input_relation, X, y)	Trains the model.
KNeighborsRegressor.get_attributes([attr_name])	Returns the model attributes.
KNeighborsRegressor.get_match_index(x, col_list)	Returns the matching index.
KNeighborsRegressor.get_params()	Returns the parameters of the model.
KNeighborsRegressor.get_plotting_lib([...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
KNeighborsRegressor.get_vertica_attributes([...])	Returns the model Vertica attributes.
KNeighborsRegressor.import_models(path[, ...])	Imports machine learning models.
KNeighborsRegressor.predict(vdf[, X, name, ...])	Predicts using the input relation.
KNeighborsRegressor.register(registered_name)	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
KNeighborsRegressor.regression_report([metrics])	Computes a regression report
KNeighborsRegressor.report([metrics])	Computes a regression report
KNeighborsRegressor.score([metric])	Computes the model score.
KNeighborsRegressor.set_params([parameters])	Sets the parameters of the model.
KNeighborsRegressor.summarize()	Summarizes the model.
KNeighborsRegressor.to_binary(path)	Exports the model to the Vertica Binary format.
KNeighborsRegressor.to_pmml(path)	Exports the model to PMML.
KNeighborsRegressor.to_python([...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
KNeighborsRegressor.to_sql([X, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
KNeighborsRegressor.to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

KNeighborsRegressor.object_type

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