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

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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

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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

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GradientBoosting Regressor

ensemble.GradientBoostingRegressor(trees[, ...])

InMemoryModel implementation of the GradientBoosting regressor algorithm.

Methods:

GradientBoostingRegressor.get_attributes()	Returns the model attributes.
GradientBoostingRegressor.plot_tree([...])	Draws the input tree.
GradientBoostingRegressor.predict(X)	Predicts using the GradientBoostingRegressor model.
GradientBoostingRegressor.predict_sql(X)	Returns the SQL code needed to deploy the model.
GradientBoostingRegressor.set_attributes(...)	Sets the model attributes.

Attributes:

GradientBoostingRegressor.object_type

Must be overridden in child class

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GradientBoosting Classifier

ensemble.GradientBoostingClassifier(trees, ...)

InMemoryModel implementation of the GradientBoosting classifier algorithm.

Methods:

GradientBoostingClassifier.get_attributes()	Returns the model attributes.
GradientBoostingClassifier.plot_tree([...])	Draws the input tree.
GradientBoostingClassifier.predict(X)	Predicts using the input matrix.
GradientBoostingClassifier.predict_proba(X)	Computes the model's probabilites using the input matrix.
GradientBoostingClassifier.predict_proba_sql(X)	Returns the SQL code needed to deploy the model using its attributes.
GradientBoostingClassifier.predict_sql(X)	Returns the SQL code needed to deploy the model.
GradientBoostingClassifier.set_attributes(...)	Sets the model attributes.

Attributes:

GradientBoostingClassifier.object_type

Must be overridden in child class

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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