skclean.simulate_noise.flip_labels_uniform¶
-
skclean.simulate_noise.
flip_labels_uniform
(Y: numpy.ndarray, noise_level: float, *, random_state=None, exact=True)¶ All labels are equally likely to be flipped, irrespective of their true label or feature. The new (noisy) label is also chosen with uniform probability from alternative class labels.
- Parameters
Y (np.ndarray) – 1-D array of labels
noise_level (float) – percentage of labels to flip
random_state (int, default=None) – Set this value for reproducibility
exact (bool default=True) – If True, the generated noise will be as close to noise_level as possible. The approximate version (i.e. exact=False) is faster but less accurate.
- Returns
Yn – 1-D array of flipped labels
- Return type
np.ndarray