Classification#

Linear Models#

Linear SVC#

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

Creates a LinearSVC object using the Vertica Support Vector Machine (SVM) algorithm on the data.

Methods:

`LinearSVC.classification_report`([metrics, ...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`LinearSVC.confusion_matrix`([cutoff])	Computes the model confusion matrix.
`LinearSVC.contour`([nbins, chart])	Draws the model's contour plot.
`LinearSVC.cutoff_curve`([nbins, show, chart])	Draws the model Cutoff curve.
`LinearSVC.deploySQL`([X, cutoff])	Returns the SQL code needed to deploy the model.
`LinearSVC.does_model_exists`(name[, ...])	Checks whether the model is stored in the Vertica database.
`LinearSVC.drop`()	Drops the model from the Vertica database.
`LinearSVC.export_models`(name, path[, kind])	Exports machine learning models.
`LinearSVC.features_importance`([show, chart])	Computes the model's features importance.
`LinearSVC.fit`(input_relation, X, y[, ...])	Trains the model.
`LinearSVC.get_attributes`([attr_name])	Returns the model attributes.
`LinearSVC.get_match_index`(x, col_list[, ...])	Returns the matching index.
`LinearSVC.get_params`()	Returns the parameters of the model.
`LinearSVC.get_plotting_lib`([class_name, ...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
`LinearSVC.get_vertica_attributes`([attr_name])	Returns the model Vertica attributes.
`LinearSVC.import_models`(path[, schema, kind])	Imports machine learning models.
`LinearSVC.lift_chart`([nbins, show, chart])	Draws the model Lift Chart.
`LinearSVC.plot`([max_nb_points, chart])	Draws the model.
`LinearSVC.prc_curve`([nbins, show, chart])	Draws the model PRC curve.
`LinearSVC.predict`(vdf[, X, name, cutoff, ...])	Makes predictions on the input relation.
`LinearSVC.predict_proba`(vdf[, X, name, ...])	Returns the model's probabilities using the input relation.
`LinearSVC.register`(registered_name[, ...])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
`LinearSVC.report`([metrics, cutoff, nbins])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`LinearSVC.roc_curve`([nbins, show, chart])	Draws the model ROC curve.
`LinearSVC.score`([metric, cutoff, nbins])	Computes the model score.
`LinearSVC.set_params`([parameters])	Sets the parameters of the model.
`LinearSVC.summarize`()	Summarizes the model.
`LinearSVC.to_binary`(path)	Exports the model to the Vertica Binary format.
`LinearSVC.to_memmodel`()	Converts the model to an InMemory object that can be used for different types of predictions.
`LinearSVC.to_pmml`(path)	Exports the model to PMML.
`LinearSVC.to_python`([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
`LinearSVC.to_sql`([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
`LinearSVC.to_tf`(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

`LinearSVC.object_type`
`LinearSVC.classes_`

Logistic Regression#

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

Creates a LogisticRegression object using the Vertica Logistic Regression algorithm.

Methods:

`LogisticRegression.classification_report`([...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`LogisticRegression.confusion_matrix`([cutoff])	Computes the model confusion matrix.
`LogisticRegression.contour`([nbins, chart])	Draws the model's contour plot.
`LogisticRegression.cutoff_curve`([nbins, ...])	Draws the model Cutoff curve.
`LogisticRegression.deploySQL`([X, cutoff])	Returns the SQL code needed to deploy the model.
`LogisticRegression.does_model_exists`(name[, ...])	Checks whether the model is stored in the Vertica database.
`LogisticRegression.drop`()	Drops the model from the Vertica database.
`LogisticRegression.export_models`(name, path)	Exports machine learning models.
`LogisticRegression.features_importance`([...])	Computes the model's features importance.
`LogisticRegression.fit`(input_relation, X, y)	Trains the model.
`LogisticRegression.get_attributes`([attr_name])	Returns the model attributes.
`LogisticRegression.get_match_index`(x, col_list)	Returns the matching index.
`LogisticRegression.get_params`()	Returns the parameters of the model.
`LogisticRegression.get_plotting_lib`([...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
`LogisticRegression.get_vertica_attributes`([...])	Returns the model Vertica attributes.
`LogisticRegression.import_models`(path[, ...])	Imports machine learning models.
`LogisticRegression.lift_chart`([nbins, show, ...])	Draws the model Lift Chart.
`LogisticRegression.plot`([max_nb_points, chart])	Draws the model.
`LogisticRegression.prc_curve`([nbins, show, ...])	Draws the model PRC curve.
`LogisticRegression.predict`(vdf[, X, name, ...])	Makes predictions on the input relation.
`LogisticRegression.predict_proba`(vdf[, X, ...])	Returns the model's probabilities using the input relation.
`LogisticRegression.register`(registered_name)	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
`LogisticRegression.report`([metrics, cutoff, ...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`LogisticRegression.roc_curve`([nbins, show, ...])	Draws the model ROC curve.
`LogisticRegression.score`([metric, cutoff, nbins])	Computes the model score.
`LogisticRegression.set_params`([parameters])	Sets the parameters of the model.
`LogisticRegression.summarize`()	Summarizes the model.
`LogisticRegression.to_binary`(path)	Exports the model to the Vertica Binary format.
`LogisticRegression.to_memmodel`()	Converts the model to an InMemory object that can be used for different types of predictions.
`LogisticRegression.to_pmml`(path)	Exports the model to PMML.
`LogisticRegression.to_python`([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
`LogisticRegression.to_sql`([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
`LogisticRegression.to_tf`(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

`LogisticRegression.object_type`
`LogisticRegression.classes_`

Tree-based algorithms#

Dummy Tree#

tree.DummyTreeClassifier([name, overwrite_model])

A classifier that overfits the training data.

Methods:

`DummyTreeClassifier.classification_report`([...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`DummyTreeClassifier.confusion_matrix`([...])	Computes the model confusion matrix.
`DummyTreeClassifier.contour`([pos_label, ...])	Draws the model's contour plot.
`DummyTreeClassifier.cutoff_curve`([...])	Draws the model Cutoff curve.
`DummyTreeClassifier.deploySQL`([X, ...])	Returns the SQL code needed to deploy the model.
`DummyTreeClassifier.does_model_exists`(name)	Checks whether the model is stored in the Vertica database.
`DummyTreeClassifier.drop`()	Drops the model from the Vertica database.
`DummyTreeClassifier.export_models`(name, path)	Exports machine learning models.
`DummyTreeClassifier.features_importance`([...])	Computes the model's features importance.
`DummyTreeClassifier.fit`(input_relation, X, y)	Trains the model.
`DummyTreeClassifier.get_attributes`([attr_name])	Returns the model attributes.
`DummyTreeClassifier.get_match_index`(x, col_list)	Returns the matching index.
`DummyTreeClassifier.get_params`()	Returns the parameters of the model.
`DummyTreeClassifier.get_plotting_lib`([...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
`DummyTreeClassifier.get_score`([tree_id])	Returns the feature importance metrics for the input tree.
`DummyTreeClassifier.get_tree`([tree_id])	Returns a table with all the input tree information.
`DummyTreeClassifier.get_vertica_attributes`([...])	Returns the model Vertica attributes.
`DummyTreeClassifier.import_models`(path[, ...])	Imports machine learning models.
`DummyTreeClassifier.lift_chart`([pos_label, ...])	Draws the model Lift Chart.
`DummyTreeClassifier.plot`([max_nb_points, chart])	Draws the model.
`DummyTreeClassifier.plot_tree`([tree_id, ...])	Draws the input tree.
`DummyTreeClassifier.prc_curve`([pos_label, ...])	Draws the model PRC curve.
`DummyTreeClassifier.predict`(vdf[, X, name, ...])	Predicts using the input relation.
`DummyTreeClassifier.predict_proba`(vdf[, X, ...])	Returns the model's probabilities using the input relation.
`DummyTreeClassifier.register`(registered_name)	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
`DummyTreeClassifier.report`([metrics, ...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`DummyTreeClassifier.roc_curve`([pos_label, ...])	Draws the model ROC curve.
`DummyTreeClassifier.score`([metric, average, ...])	Computes the model score.
`DummyTreeClassifier.set_params`([parameters])	Sets the parameters of the model.
`DummyTreeClassifier.summarize`()	Summarizes the model.
`DummyTreeClassifier.to_binary`(path)	Exports the model to the Vertica Binary format.
`DummyTreeClassifier.to_graphviz`([tree_id, ...])	Returns the code for a Graphviz tree.
`DummyTreeClassifier.to_memmodel`()	Converts the model to an InMemory object that can be used for different types of predictions.
`DummyTreeClassifier.to_pmml`(path)	Exports the model to PMML.
`DummyTreeClassifier.to_python`([...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
`DummyTreeClassifier.to_sql`([X, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
`DummyTreeClassifier.to_tf`(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

DummyTreeClassifier.object_type

Decision Tree#

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

A DecisionTreeClassifier consisting of a single tree.

Methods:

`DecisionTreeClassifier.classification_report`([...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`DecisionTreeClassifier.confusion_matrix`([...])	Computes the model confusion matrix.
`DecisionTreeClassifier.contour`([pos_label, ...])	Draws the model's contour plot.
`DecisionTreeClassifier.cutoff_curve`([...])	Draws the model Cutoff curve.
`DecisionTreeClassifier.deploySQL`([X, ...])	Returns the SQL code needed to deploy the model.
`DecisionTreeClassifier.does_model_exists`(name)	Checks whether the model is stored in the Vertica database.
`DecisionTreeClassifier.drop`()	Drops the model from the Vertica database.
`DecisionTreeClassifier.export_models`(name, path)	Exports machine learning models.
`DecisionTreeClassifier.features_importance`([...])	Computes the model's features importance.
`DecisionTreeClassifier.fit`(input_relation, X, y)	Trains the model.
`DecisionTreeClassifier.get_attributes`([...])	Returns the model attributes.
`DecisionTreeClassifier.get_match_index`(x, ...)	Returns the matching index.
`DecisionTreeClassifier.get_params`()	Returns the parameters of the model.
`DecisionTreeClassifier.get_plotting_lib`([...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
`DecisionTreeClassifier.get_score`([tree_id])	Returns the feature importance metrics for the input tree.
`DecisionTreeClassifier.get_tree`([tree_id])	Returns a table with all the input tree information.
`DecisionTreeClassifier.get_vertica_attributes`([...])	Returns the model Vertica attributes.
`DecisionTreeClassifier.import_models`(path[, ...])	Imports machine learning models.
`DecisionTreeClassifier.lift_chart`([...])	Draws the model Lift Chart.
`DecisionTreeClassifier.plot`([max_nb_points, ...])	Draws the model.
`DecisionTreeClassifier.plot_tree`([tree_id, ...])	Draws the input tree.
`DecisionTreeClassifier.prc_curve`([...])	Draws the model PRC curve.
`DecisionTreeClassifier.predict`(vdf[, X, ...])	Predicts using the input relation.
`DecisionTreeClassifier.predict_proba`(vdf[, ...])	Returns the model's probabilities using the input relation.
`DecisionTreeClassifier.register`(registered_name)	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
`DecisionTreeClassifier.report`([metrics, ...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`DecisionTreeClassifier.roc_curve`([...])	Draws the model ROC curve.
`DecisionTreeClassifier.score`([metric, ...])	Computes the model score.
`DecisionTreeClassifier.set_params`([parameters])	Sets the parameters of the model.
`DecisionTreeClassifier.summarize`()	Summarizes the model.
`DecisionTreeClassifier.to_binary`(path)	Exports the model to the Vertica Binary format.
`DecisionTreeClassifier.to_graphviz`([...])	Returns the code for a Graphviz tree.
`DecisionTreeClassifier.to_memmodel`()	Converts the model to an InMemory object that can be used for different types of predictions.
`DecisionTreeClassifier.to_pmml`(path)	Exports the model to PMML.
`DecisionTreeClassifier.to_python`([...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
`DecisionTreeClassifier.to_sql`([X, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
`DecisionTreeClassifier.to_tf`(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

DecisionTreeClassifier.object_type

Random Forest Classifier#

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

Creates a RandomForestClassifier object using the Vertica RF_CLASSIFIER function.

Methods:

`RandomForestClassifier.classification_report`([...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`RandomForestClassifier.confusion_matrix`([...])	Computes the model confusion matrix.
`RandomForestClassifier.contour`([pos_label, ...])	Draws the model's contour plot.
`RandomForestClassifier.cutoff_curve`([...])	Draws the model Cutoff curve.
`RandomForestClassifier.deploySQL`([X, ...])	Returns the SQL code needed to deploy the model.
`RandomForestClassifier.does_model_exists`(name)	Checks whether the model is stored in the Vertica database.
`RandomForestClassifier.drop`()	Drops the model from the Vertica database.
`RandomForestClassifier.export_models`(name, path)	Exports machine learning models.
`RandomForestClassifier.features_importance`([...])	Computes the model's features importance.
`RandomForestClassifier.fit`(input_relation, X, y)	Trains the model.
`RandomForestClassifier.get_attributes`([...])	Returns the model attributes.
`RandomForestClassifier.get_match_index`(x, ...)	Returns the matching index.
`RandomForestClassifier.get_params`()	Returns the parameters of the model.
`RandomForestClassifier.get_plotting_lib`([...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
`RandomForestClassifier.get_score`([tree_id])	Returns the feature importance metrics for the input tree.
`RandomForestClassifier.get_tree`([tree_id])	Returns a table with all the input tree information.
`RandomForestClassifier.get_vertica_attributes`([...])	Returns the model Vertica attributes.
`RandomForestClassifier.import_models`(path[, ...])	Imports machine learning models.
`RandomForestClassifier.lift_chart`([...])	Draws the model Lift Chart.
`RandomForestClassifier.plot`([max_nb_points, ...])	Draws the model.
`RandomForestClassifier.plot_tree`([tree_id, ...])	Draws the input tree.
`RandomForestClassifier.prc_curve`([...])	Draws the model PRC curve.
`RandomForestClassifier.predict`(vdf[, X, ...])	Predicts using the input relation.
`RandomForestClassifier.predict_proba`(vdf[, ...])	Returns the model's probabilities using the input relation.
`RandomForestClassifier.register`(registered_name)	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
`RandomForestClassifier.report`([metrics, ...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`RandomForestClassifier.roc_curve`([...])	Draws the model ROC curve.
`RandomForestClassifier.score`([metric, ...])	Computes the model score.
`RandomForestClassifier.set_params`([parameters])	Sets the parameters of the model.
`RandomForestClassifier.summarize`()	Summarizes the model.
`RandomForestClassifier.to_binary`(path)	Exports the model to the Vertica Binary format.
`RandomForestClassifier.to_graphviz`([...])	Returns the code for a Graphviz tree.
`RandomForestClassifier.to_memmodel`()	Converts the model to an InMemory object that can be used for different types of predictions.
`RandomForestClassifier.to_pmml`(path)	Exports the model to PMML.
`RandomForestClassifier.to_python`([...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
`RandomForestClassifier.to_sql`([X, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
`RandomForestClassifier.to_tf`(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

RandomForestClassifier.object_type

XGBoost Classifier#

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

Creates an XGBClassifier object using the Vertica XGB_CLASSIFIER algorithm.

Methods:

`XGBClassifier.classification_report`([...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`XGBClassifier.confusion_matrix`([pos_label, ...])	Computes the model confusion matrix.
`XGBClassifier.contour`([pos_label, nbins, chart])	Draws the model's contour plot.
`XGBClassifier.cutoff_curve`([pos_label, ...])	Draws the model Cutoff curve.
`XGBClassifier.deploySQL`([X, pos_label, ...])	Returns the SQL code needed to deploy the model.
`XGBClassifier.does_model_exists`(name[, ...])	Checks whether the model is stored in the Vertica database.
`XGBClassifier.drop`()	Drops the model from the Vertica database.
`XGBClassifier.export_models`(name, path[, kind])	Exports machine learning models.
`XGBClassifier.features_importance`([tree_id, ...])	Computes the model's features importance.
`XGBClassifier.fit`(input_relation, X, y[, ...])	Trains the model.
`XGBClassifier.get_attributes`([attr_name])	Returns the model attributes.
`XGBClassifier.get_match_index`(x, col_list[, ...])	Returns the matching index.
`XGBClassifier.get_params`()	Returns the parameters of the model.
`XGBClassifier.get_plotting_lib`([class_name, ...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
`XGBClassifier.get_score`([tree_id])	Returns the feature importance metrics for the input tree.
`XGBClassifier.get_tree`([tree_id])	Returns a table with all the input tree information.
`XGBClassifier.get_vertica_attributes`([attr_name])	Returns the model Vertica attributes.
`XGBClassifier.import_models`(path[, schema, kind])	Imports machine learning models.
`XGBClassifier.lift_chart`([pos_label, nbins, ...])	Draws the model Lift Chart.
`XGBClassifier.plot`([max_nb_points, chart])	Draws the model.
`XGBClassifier.plot_tree`([tree_id, pic_path])	Draws the input tree.
`XGBClassifier.prc_curve`([pos_label, nbins, ...])	Draws the model PRC curve.
`XGBClassifier.predict`(vdf[, X, name, ...])	Predicts using the input relation.
`XGBClassifier.predict_proba`(vdf[, X, name, ...])	Returns the model's probabilities using the input relation.
`XGBClassifier.register`(registered_name[, ...])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
`XGBClassifier.report`([metrics, cutoff, ...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`XGBClassifier.roc_curve`([pos_label, nbins, ...])	Draws the model ROC curve.
`XGBClassifier.score`([metric, average, ...])	Computes the model score.
`XGBClassifier.set_params`([parameters])	Sets the parameters of the model.
`XGBClassifier.summarize`()	Summarizes the model.
`XGBClassifier.to_binary`(path)	Exports the model to the Vertica Binary format.
`XGBClassifier.to_graphviz`([tree_id, ...])	Returns the code for a Graphviz tree.
`XGBClassifier.to_json`([path])	Creates a Python `XGBoost` JSON file
`XGBClassifier.to_memmodel`()	Converts the model to an InMemory object that can be used for different types of predictions.
`XGBClassifier.to_pmml`(path)	Exports the model to PMML.
`XGBClassifier.to_python`([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
`XGBClassifier.to_sql`([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
`XGBClassifier.to_tf`(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

XGBClassifier.object_type

Naive Bayes#

naive_bayes.NaiveBayes([name, ...])

Creates a NaiveBayes object using the Vertica Naive Bayes algorithm.

Methods:

`NaiveBayes.classification_report`([metrics, ...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`NaiveBayes.confusion_matrix`([pos_label, cutoff])	Computes the model confusion matrix.
`NaiveBayes.contour`([pos_label, nbins, chart])	Draws the model's contour plot.
`NaiveBayes.cutoff_curve`([pos_label, nbins, ...])	Draws the model Cutoff curve.
`NaiveBayes.deploySQL`([X, pos_label, cutoff, ...])	Returns the SQL code needed to deploy the model.
`NaiveBayes.does_model_exists`(name[, ...])	Checks whether the model is stored in the Vertica database.
`NaiveBayes.drop`()	Drops the model from the Vertica database.
`NaiveBayes.export_models`(name, path[, kind])	Exports machine learning models.
`NaiveBayes.fit`(input_relation, X, y[, ...])	Trains the model.
`NaiveBayes.get_attributes`([attr_name])	Returns the model attributes.
`NaiveBayes.get_match_index`(x, col_list[, ...])	Returns the matching index.
`NaiveBayes.get_params`()	Returns the parameters of the model.
`NaiveBayes.get_plotting_lib`([class_name, ...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
`NaiveBayes.get_vertica_attributes`([attr_name])	Returns the model Vertica attributes.
`NaiveBayes.import_models`(path[, schema, kind])	Imports machine learning models.
`NaiveBayes.lift_chart`([pos_label, nbins, ...])	Draws the model Lift Chart.
`NaiveBayes.prc_curve`([pos_label, nbins, ...])	Draws the model PRC curve.
`NaiveBayes.predict`(vdf[, X, name, cutoff, ...])	Predicts using the input relation.
`NaiveBayes.predict_proba`(vdf[, X, name, ...])	Returns the model's probabilities using the input relation.
`NaiveBayes.register`(registered_name[, ...])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
`NaiveBayes.report`([metrics, cutoff, labels, ...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`NaiveBayes.roc_curve`([pos_label, nbins, ...])	Draws the model ROC curve.
`NaiveBayes.score`([metric, average, ...])	Computes the model score.
`NaiveBayes.set_params`([parameters])	Sets the parameters of the model.
`NaiveBayes.summarize`()	Summarizes the model.
`NaiveBayes.to_binary`(path)	Exports the model to the Vertica Binary format.
`NaiveBayes.to_memmodel`()	Converts the model to an InMemory object that can be used for different types of predictions.
`NaiveBayes.to_pmml`(path)	Exports the model to PMML.
`NaiveBayes.to_python`([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
`NaiveBayes.to_sql`([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
`NaiveBayes.to_tf`(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

NaiveBayes.object_type

Neighbors#

K-Nearest Neighbors Classifier (Beta)#

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

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

Methods:

`KNeighborsClassifier.classification_report`([...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`KNeighborsClassifier.confusion_matrix`([...])	Computes the model confusion matrix.
`KNeighborsClassifier.contour`([pos_label, ...])	Draws the model's contour plot.
`KNeighborsClassifier.cutoff_curve`([...])	Draws the model Cutoff curve.
`KNeighborsClassifier.deploySQL`([X, ...])	Returns the SQL code needed to deploy the model.
`KNeighborsClassifier.does_model_exists`(name)	Checks whether the model is stored in the Vertica database.
`KNeighborsClassifier.drop`()	`KNeighborsClassifier` models are not stored in the Vertica DB.
`KNeighborsClassifier.export_models`(name, path)	Exports machine learning models.
`KNeighborsClassifier.fit`(input_relation, X, y)	Trains the model.
`KNeighborsClassifier.get_attributes`([attr_name])	Returns the model attributes.
`KNeighborsClassifier.get_match_index`(x, col_list)	Returns the matching index.
`KNeighborsClassifier.get_params`()	Returns the parameters of the model.
`KNeighborsClassifier.get_plotting_lib`([...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
`KNeighborsClassifier.get_vertica_attributes`([...])	Returns the model Vertica attributes.
`KNeighborsClassifier.import_models`(path[, ...])	Imports machine learning models.
`KNeighborsClassifier.lift_chart`([pos_label, ...])	Draws the model Lift Chart.
`KNeighborsClassifier.prc_curve`([pos_label, ...])	Draws the model PRC curve.
`KNeighborsClassifier.predict`(vdf[, X, name, ...])	Predicts using the input relation.
`KNeighborsClassifier.predict_proba`(vdf[, X, ...])	Returns the model's probabilities using the input relation.
`KNeighborsClassifier.register`(registered_name)	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
`KNeighborsClassifier.report`([metrics, ...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`KNeighborsClassifier.roc_curve`([pos_label, ...])	Draws the model ROC curve.
`KNeighborsClassifier.score`([metric, ...])	Computes the model score.
`KNeighborsClassifier.set_params`([parameters])	Sets the parameters of the model.
`KNeighborsClassifier.summarize`()	Summarizes the model.
`KNeighborsClassifier.to_binary`(path)	Exports the model to the Vertica Binary format.
`KNeighborsClassifier.to_pmml`(path)	Exports the model to PMML.
`KNeighborsClassifier.to_python`([...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
`KNeighborsClassifier.to_sql`([X, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
`KNeighborsClassifier.to_tf`(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

KNeighborsClassifier.object_type

Nearest Centroid (Beta)#

cluster.NearestCentroid([name, ...])

Creates a NearestCentroid object using the k-nearest centroid algorithm.

Methods:

`NearestCentroid.classification_report`([...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`NearestCentroid.confusion_matrix`([...])	Computes the model confusion matrix.
`NearestCentroid.contour`([pos_label, nbins, ...])	Draws the model's contour plot.
`NearestCentroid.cutoff_curve`([pos_label, ...])	Draws the model Cutoff curve.
`NearestCentroid.deploySQL`([X, pos_label, ...])	Returns the SQL code needed to deploy the model.
`NearestCentroid.does_model_exists`(name[, ...])	Checks whether the model is stored in the Vertica database.
`NearestCentroid.drop`()	`NearestCentroid` models are not stored in the Vertica DB.
`NearestCentroid.export_models`(name, path[, kind])	Exports machine learning models.
`NearestCentroid.fit`(input_relation, X, y[, ...])	Trains the model.
`NearestCentroid.get_attributes`([attr_name])	Returns the model attributes.
`NearestCentroid.get_match_index`(x, col_list)	Returns the matching index.
`NearestCentroid.get_params`()	Returns the parameters of the model.
`NearestCentroid.get_plotting_lib`([...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
`NearestCentroid.get_vertica_attributes`([...])	Returns the model Vertica attributes.
`NearestCentroid.import_models`(path[, ...])	Imports machine learning models.
`NearestCentroid.lift_chart`([pos_label, ...])	Draws the model Lift Chart.
`NearestCentroid.prc_curve`([pos_label, ...])	Draws the model PRC curve.
`NearestCentroid.predict`(vdf[, X, name, ...])	Predicts using the input relation.
`NearestCentroid.predict_proba`(vdf[, X, ...])	Returns the model's probabilities using the input relation.
`NearestCentroid.register`(registered_name[, ...])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
`NearestCentroid.report`([metrics, cutoff, ...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`NearestCentroid.roc_curve`([pos_label, ...])	Draws the model ROC curve.
`NearestCentroid.score`([metric, average, ...])	Computes the model score.
`NearestCentroid.set_params`([parameters])	Sets the parameters of the model.
`NearestCentroid.summarize`()	Summarizes the model.
`NearestCentroid.to_binary`(path)	Exports the model to the Vertica Binary format.
`NearestCentroid.to_memmodel`()	Converts the model to an InMemory object that can be used for different types of predictions.
`NearestCentroid.to_pmml`(path)	Exports the model to PMML.
`NearestCentroid.to_python`([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
`NearestCentroid.to_sql`([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
`NearestCentroid.to_tf`(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

NearestCentroid.object_type