Update Classifier docstrings
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@ -20,9 +20,8 @@ class CatboostClassifier(BaseClassifierModel):
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def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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User sets up the training and test data to fit their desired model here
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:params:
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:data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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:param data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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"""
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train_data = Pool(
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@ -20,9 +20,8 @@ class LightGBMClassifier(BaseClassifierModel):
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def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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User sets up the training and test data to fit their desired model here
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:params:
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:data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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:param data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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"""
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if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
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@ -26,9 +26,8 @@ class XGBoostClassifier(BaseClassifierModel):
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def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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User sets up the training and test data to fit their desired model here
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:params:
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:data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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:param data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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"""
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X = data_dictionary["train_features"].to_numpy()
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