Generating artificial dataset with outliers

dataset_with_outliers.py

import numpy as np
from sklearn.datasets import make_classification

# Data with features in different scales
n_classes = 2
X_clean, y_clean = make_classification(
    n_samples=500, n_features=2, n_redundant=0,
    scale=(10, 100), random_state=0)

# Add outliers to the data
X_outliers, y_outliers = make_classification(
    n_samples=10, n_features=2, n_redundant=0,
    # scale=(10, 100), random_state=1)
    scale=(-100, 1000), random_state=1)

X = np.concatenate((X_clean, X_outliers), axis=0)
y = np.concatenate((y_clean, y_outliers), axis=0)