`aif360.datasets`.BankDataset

class aif360.datasets.BankDataset(label_name='y', favorable_classes=['yes'], protected_attribute_names=['age'], privileged_classes=[<function BankDataset.<lambda>>], instance_weights_name=None, categorical_features=['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'poutcome'], features_to_keep=[], features_to_drop=[], na_values=['unknown'], custom_preprocessing=None, metadata=None)[source]

Bank marketing Dataset.

See aif360/data/raw/bank/README.md.

See StandardDataset for a description of the arguments.

By default, this code converts the ‘age’ attribute to a binary value where privileged is 25 <= age < 60 and unprivileged is age < 25 or age >= 60 as suggested in Le Quy, Tai, et al. [1].

References

learning.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12.3 (2022): e1452.

Methods

`align_datasets`	Align the other dataset features, labels and protected_attributes to this dataset.
`convert_to_dataframe`	Convert the StructuredDataset to a `pandas.DataFrame`.
`copy`	Convenience method to return a copy of this dataset.
`export_dataset`	Export the dataset and supporting attributes TODO: The preferred file format is HDF
`import_dataset`	Import the dataset and supporting attributes TODO: The preferred file format is HDF
`split`	Split this dataset into multiple partitions.
`subset`	Subset of dataset based on position :param indexes: iterable which contains row indexes
`temporarily_ignore`	Temporarily add the fields provided to `ignore_fields`.
`validate_dataset`	Error checking and type validation.

__init__(label_name='y', favorable_classes=['yes'], protected_attribute_names=['age'], privileged_classes=[<function BankDataset.<lambda>>], instance_weights_name=None, categorical_features=['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'poutcome'], features_to_keep=[], features_to_drop=[], na_values=['unknown'], custom_preprocessing=None, metadata=None)[source]