In this article, we are going to see how to create simulated data for classification in Python.
We will use the sklearn library that provides various generators for simulating classification data.
Single Label Classification
Here we are going to see single-label classification, for this we will use some visualization techniques.
Example 1: Using make_circles()
make_circles generates 2d binary classification data with a spherical decision boundary.
Python3
from sklearn.datasets import make_circles import pandas as pd import matplotlib.pyplot as plt X, y = make_circles(n_samples=200, shuffle=True, noise=0.1, random_state=42) plt.scatter(X[:, 0], X[:, 1], c=y) plt.show() |
Output:
Example 2: Using make_moons()
make_moons() generates 2d binary classification data in the shape of two interleaving half circles.
Python3
from sklearn.datasets import make_moons import pandas as pd import matplotlib.pyplot as plt X, y = make_moons(n_samples=200, shuffle=True, noise=0.15, random_state=42) plt.scatter(X[:, 0], X[:, 1], c=y) plt.show() |
Output:
Example 3. Using make_blobs()
make_blobs() generates data in form of blobs that can be used for clustering
Python3
from sklearn.datasets import make_blobs import pandas as pd import matplotlib.pyplot as plt X, y = make_blobs(n_samples=200, n_features=2, centers=3, shuffle=True, random_state=42) plt.scatter(X[:, 0], X[:, 1], c=y) plt.show() |
Output:
Example 4. Using make_classification()
make_classification() generates a random n-class classification problem
Python3
from sklearn.datasets import make_classification import pandas as pd import matplotlib.pyplot as plt X, y = make_classification(n_samples=100, n_features=5, n_classes=2, n_informative=2, n_redundant=2, n_repeated=0, shuffle=True, random_state=42) pd.concat([pd.DataFrame(X), pd.DataFrame( y, columns=['Label'])], axis=1) |
Output:
Multi-Label Classification
make_multilabel_classification() generates a random multi-label classification problem.
Python3
from sklearn.datasets import make_multilabel_classification import pandas as pd import matplotlib.pyplot as plt X, y = make_multilabel_classification(n_samples=100, n_features=5, n_classes=2, n_labels=1, allow_unlabeled=False, random_state=42) pd.concat([pd.DataFrame(X), pd.DataFrame(y, columns=['L1', 'L2'])], axis=1) |
Output:
