feyn.datasets

function make_classification

def make_classification(
    n_samples: int = 100,
    n_features: int = 20,
    stratify: bool = False,
    shuffle_features: bool = False,
    shuffle_split: bool = True,
    random_state: Optional[bool] = None,
    **kwargs
) -> Tuple

Uses sklearn.datasets.make_classification to create a classification problem
and returns train and test DataFrames.
Keyword arguments are parameters in sklearn.datasets.make_classification and
sklearn.model_selection.train_test_split.

Args:
    n_samples (int, optional): The number of samples. Defaults to 100.
    n_features (int, optional): The number of features. Defaults to 20.
    stratify (bool, optional): Stratifies the train, test split by the output variable y. Defaults to False.
    shuffle_features (bool, optional): Whether or not to shuffle the samples and the features. Defaults to False.
    shuffle_split (bool, optional): Whether or not to shuffle the data before doing the train-test split. Defaults to True.
    random_state (int or None, optional): Determines the seed for the randomness associated with creating an splitting the synthetic data set. Defaults to None.

Returns:
    train, test: The training and test set of the classification problem

function make_regression

def make_regression(
    n_samples: int = 100,
    n_features: int = 20,
    stratify: bool = False,
    shuffle_features: bool = False,
    shuffle_split: bool = True,
    random_state: Optional[bool] = None,
    **kwargs
) -> Tuple

Uses sklearn.datasets.make_regression to create a regression problem
and returns train and test DataFrames.
Keyword arguments are parameters in sklearn.datasets.make_regression and
sklearn.model_selection.train_test_split.

Args:
    n_samples (int, optional): The number of samples. Defaults to 100.
    n_features (int, optional): The number of features. Defaults to 20.
    stratify (bool, optional): Stratifies the train, test split by the output variable y. Defaults to False.
    shuffle_features (bool, optional): Whether or not to shuffle the samples and the features. Defaults to False.
    shuffle_split (bool, optional): Whether or not to shuffle the data before doing the train-test split. Defaults to True.
    random_state (int or None, optional): Determines the seed for the randomness associated with creating an splitting the synthetic data set. Defaults to None.

Returns:
    Tuple: [description]