make_classification()
make_regression()
make_blobs()
all above
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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
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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.