(self)
return (
            ("Number of trees", self.n_estimators),
            ("Maximal number of considered features",
             self.max_features if self.use_max_features else "unlimited"),
            ("Replicable training", ["No", "Yes"][self.use_random_state]),
            ("Maximal tree depth",
             self.max_depth if self.use_max_depth else "unlimited"),
            ("Stop splitting nodes with maximum instances",
             self.min_samples_split if self.use_min_samples_split else
             "unlimited"),
            ("Class weights", self.class_weight)
        )
Called by send report to list the parameters of the learner.
Called by send report to list the parameters of the learner.
Called by send report to list the parameters of the learner .
def get_learner_parameters(self):
        """Called by send report to list the parameters of the learner."""
        return (
            ("Number of trees", self.n_estimators),
            ("Maximal number of considered features",
             self.max_features if self.use_max_features else "unlimited"),
            ("Replicable training", ["No", "Yes"][self.use_random_state]),
            ("Maximal tree depth",
             self.max_depth if self.use_max_depth else "unlimited"),
            ("Stop splitting nodes with maximum instances",
             self.min_samples_split if self.use_min_samples_split else
             "unlimited"),
            ("Class weights", self.class_weight)
        )
def get_learner_parameters ( self ) : return ( ( "Number of trees" , self . n_estimators ) , ( "Maximal number of considered features" , self . max_features if self . use_max_features else "unlimited" ) , ( "Replicable training" , [ "No" , "Yes" ] [ self . use_random_state ] ) , ( "Maximal tree depth" , self . max_depth if self . use_max_depth else "unlimited" ) , ( "Stop splitting nodes with maximum instances" , self . min_samples_split if self . use_min_samples_split else "unlimited" ) , ( "Class weights" , self . class_weight ) )