예제 #1
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    def __init__(self,
                 imbalance_upsampling=None,
                 class_weight=None,
                 method=None,
                 random_state=10,
                 log=None):

        MlModelCommon.__init__(self,
                               imbalance_upsampling=imbalance_upsampling,
                               class_weight=class_weight,
                               method=method,
                               log=log)

        if method == "Bagging":
            model = DecisionTreeClassifier(class_weight=class_weight,
                                           min_samples_split=20,
                                           random_state=99)
            self.ensemble_method = BaggingClassifier(base_estimator=model,
                                                     n_estimators=10,
                                                     random_state=random_state)
        elif method == "Adaptive Boosting":
            model = DecisionTreeClassifier(class_weight=class_weight,
                                           min_samples_split=20,
                                           random_state=99)
            self.ensemble_method = AdaBoostClassifier(
                base_estimator=model,
                n_estimators=50,
                random_state=random_state)
        else:
            self.ensemble_method = None
            DecisionTreeClassifier.__init__(self,
                                            class_weight=class_weight,
                                            min_samples_split=20,
                                            random_state=99)
예제 #2
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    def __init__(self, **kwargs):

        DecisionTreeClassifier.__init__(self, **kwargs)

        return
예제 #3
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 def __init__(self,threshold=1,ll_ranking=False,**kwargs):
     DT.__init__(self,**kwargs)
     BaseClassifier.__init__(self,threshold=threshold,ll_ranking=ll_ranking)
예제 #4
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 def __init__(self):
     """
     constructor
     """
     DecisionTreeClassifier.__init__(self, max_depth=5)
예제 #5
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 def __init__(self, dataset, use_relevance=False):
     DecisionTreeClassifier.__init__(self)
     self.n_inputs = dataset.data.shape[1]
     self.n_targets = len(dataset.target_names)
     self.use_relevance = use_relevance