scikit-learn also provides functions for creating dummy datasets from scratch:

The correct answer is D. all above.

scikit-learn is a free and open-source machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

scikit-learn also provides functions for creating dummy datasets from scratch. The make_classification() function creates a dataset with a specified number of classes and features. The make_regression() function creates a dataset with a specified number of features and a continuous target variable. The make_blobs() function creates a dataset with a specified number of clusters.

Here is an example of how to use the make_classification() function to create a dataset with 1000 samples, 2 features, and 3 classes:

“`
from sklearn.datasets import make_classification

X, y = make_classification(n_samples=1000, n_features=2, n_classes=3)
“`

The X array will contain the features of the dataset, and the y array will contain the class labels.

Here is an example of how to use the make_regression() function to create a dataset with 1000 samples, 2 features, and a continuous target variable:

“`
from sklearn.datasets import make_regression

X, y = make_regression(n_samples=1000, n_features=2)
“`

The X array will contain the features of the dataset, and the y array will contain the target variable.

Here is an example of how to use the make_blobs() function to create a dataset with 1000 samples, 2 features, and 3 clusters:

“`
from sklearn.datasets import make_blobs

X, y = make_blobs(n_samples=1000, n_features=2, n_clusters=3)
“`

The X array will contain the features of the dataset, and the y array will contain the cluster labels.