def predict(self,
dataset,
name,
target,
nb_minimizations=1,
coverage_increment=0.01):
"""
Create a prediction model from the ruleset
Args:
dataset (Dataset): Dataset to apply the prediction
name (str): Name of the new model
target (Target): Target used to generate the model
nb_minimizations (int): Number of minimizations to perform on the ruleset, default is 1
coverage_increment (float): Percentage increment of target samples that a new rule must bring to be added to the minimized ruleset,
default is 0.01
Returns:
Model or None if Ruleset is deleted / in error
"""
if not self._is_deleted and not self._is_in_error:
return ModelFactory(self.__api,
self.project_id).predict_from_ruleset(
self.__dataset, dataset, self.name, name,
target, nb_minimizations,
coverage_increment)
return None
def Model(self):
"""
This object includes utilities for creating and retrieving existing models in this project.
Returns:
An object of type ModelFactory
"""
return ModelFactory(self.__api, self.project_id)
def Model(self):
"""
ModelFactory: Tool class for creating and retrieving existing models in this project
"""
return ModelFactory(self.__api, self.project_id)