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Ensemble#

Base Class#

ensemble.Ensemble()

InMemoryModel implementation of ensemble algorithms.

Methods:

Ensemble.get_attributes()

Returns the model attributes.

Ensemble.plot_tree([pic_path, tree_id])

Draws the input tree.

Ensemble.set_attributes(**kwargs)

Sets the model attributes.

Attributes:

Ensemble.object_type

Must be overridden in child class

Random Forest Regressor#

ensemble.RandomForestRegressor(trees)

InMemoryModel implementation of the random forest regressor algorithm.

Methods:

RandomForestRegressor.get_attributes()

Returns the model attributes.

RandomForestRegressor.plot_tree([pic_path, ...])

Draws the input tree.

RandomForestRegressor.predict(X)

Predicts using the Random Forest regressor model.

RandomForestRegressor.predict_sql(X)

Returns the SQL code needed to deploy the model.

RandomForestRegressor.set_attributes(**kwargs)

Sets the model attributes.

Attributes:

RandomForestRegressor.object_type

Must be overridden in child class

Random Forest Classifier#

ensemble.RandomForestClassifier(trees[, classes])

InMemoryModel implementation of the random forest classifier algorithm.

Methods:

RandomForestClassifier.get_attributes()

Returns the model attributes.

RandomForestClassifier.plot_tree([pic_path, ...])

Draws the input tree.

RandomForestClassifier.predict(X)

Predicts using the input matrix.

RandomForestClassifier.predict_proba(X)

Computes the model's probabilites using the input matrix.

RandomForestClassifier.predict_proba_sql(X)

Returns the SQL code needed to deploy the model using its attributes.

RandomForestClassifier.predict_sql(X)

Returns the SQL code needed to deploy the model.

RandomForestClassifier.set_attributes(**kwargs)

Sets the model attributes.

Attributes:

RandomForestClassifier.object_type

Must be overridden in child class

XGBoost Regressor#

ensemble.XGBRegressor(trees[, mean, eta])

InMemoryModel implementation of the XGBoost regressor algorithm.

Methods:

XGBRegressor.get_attributes()

Returns the model attributes.

XGBRegressor.plot_tree([pic_path, tree_id])

Draws the input tree.

XGBRegressor.predict(X)

Predicts using the XGBRegressor model.

XGBRegressor.predict_sql(X)

Returns the SQL code needed to deploy the model.

XGBRegressor.set_attributes(**kwargs)

Sets the model attributes.

Attributes:

XGBRegressor.object_type

Must be overridden in child class

XGBoost Classifier#

ensemble.XGBClassifier(trees, logodds[, ...])

InMemoryModel implementation of the XGBoost classifier algorithm.

Methods:

XGBClassifier.get_attributes()

Returns the model attributes.

XGBClassifier.plot_tree([pic_path, tree_id])

Draws the input tree.

XGBClassifier.predict(X)

Predicts using the input matrix.

XGBClassifier.predict_proba(X)

Computes the model's probabilites using the input matrix.

XGBClassifier.predict_proba_sql(X)

Returns the SQL code needed to deploy the model using its attributes.

XGBClassifier.predict_sql(X)

Returns the SQL code needed to deploy the model.

XGBClassifier.set_attributes(**kwargs)

Sets the model attributes.

Attributes:

XGBClassifier.object_type

Must be overridden in child class

Isolation Forest#

ensemble.IsolationForest(trees)

InMemoryModel implementation of the isolation forest algorithm.

Methods:

IsolationForest.get_attributes()

Returns the model attributes.

IsolationForest.plot_tree([pic_path, tree_id])

Draws the input tree.

IsolationForest.predict(X)

Predicts using the IsolationForest model.

IsolationForest.predict_sql(X)

Returns the SQL code needed to deploy the model.

IsolationForest.set_attributes(**kwargs)

Sets the model attributes.

Attributes:

IsolationForest.object_type

Must be overridden in child class