tabular/src/autogluon/tabular/models/catboost/catboost_model.py [61:75]:
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    def _estimate_memory_usage(self, X, **kwargs):
        num_classes = self.num_classes if self.num_classes else 1  # self.num_classes could be None after initalization if it's a regression problem
        data_mem_uasge = get_approximate_df_mem_usage(X).sum()
        approx_mem_size_req = data_mem_uasge * 7 + data_mem_uasge / 4 * num_classes  # TODO: Extremely crude approximation, can be vastly improved
        return approx_mem_size_req

    # TODO: Use Pool in preprocess, optimize bagging to do Pool.split() to avoid re-computing pool for each fold! Requires stateful + y
    #  Pool is much more memory efficient, avoids copying data twice in memory
    def _fit(self,
             X,
             y,
             X_val=None,
             y_val=None,
             time_limit=None,
             num_gpus=0,
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tabular/src/autogluon/tabular/models/lgb/lgb_model.py [65:77]:
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    def _estimate_memory_usage(self, X, **kwargs):
        num_classes = self.num_classes if self.num_classes else 1  # self.num_classes could be None after initalization if it's a regression problem
        data_mem_uasge = get_approximate_df_mem_usage(X).sum()
        approx_mem_size_req = data_mem_uasge * 7 + data_mem_uasge / 4 * num_classes  # TODO: Extremely crude approximation, can be vastly improved
        return approx_mem_size_req

    def _fit(self,
             X,
             y,
             X_val=None,
             y_val=None,
             time_limit=None,
             num_gpus=0,
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