[amp_mcq option1=”make_classification()” option2=”make_regression()” option3=”make_blobs()” option4=”all above” correct=”option4″]<\/p>\n
The correct answer is D. all above.<\/p>\n
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.<\/p>\n
scikit-learn also provides functions for creating dummy datasets from scratch. The Here is an example of how to use the “` X, y = make_classification(n_samples=1000, n_features=2, n_classes=3) The Here is an example of how to use the “` X, y = make_regression(n_samples=1000, n_features=2) The Here is an example of how to use the “` X, y = make_blobs(n_samples=1000, n_features=2, n_clusters=3) The [amp_mcq option1=”make_classification()” option2=”make_regression()” option3=”make_blobs()” option4=”all above” correct=”option4″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[729],"tags":[],"class_list":["post-54677","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\nmake_classification()<\/code> function creates a dataset with a specified number of classes and features. The make_regression()<\/code> function creates a dataset with a specified number of features and a continuous target variable. The make_blobs()<\/code> function creates a dataset with a specified number of clusters.<\/p>\nmake_classification()<\/code> function to create a dataset with 1000 samples, 2 features, and 3 classes:<\/p>\n
\nfrom sklearn.datasets import make_classification<\/p>\n
\n“`<\/p>\nX<\/code> array will contain the features of the dataset, and the y<\/code> array will contain the class labels.<\/p>\nmake_regression()<\/code> function to create a dataset with 1000 samples, 2 features, and a continuous target variable:<\/p>\n
\nfrom sklearn.datasets import make_regression<\/p>\n
\n“`<\/p>\nX<\/code> array will contain the features of the dataset, and the y<\/code> array will contain the target variable.<\/p>\nmake_blobs()<\/code> function to create a dataset with 1000 samples, 2 features, and 3 clusters:<\/p>\n
\nfrom sklearn.datasets import make_blobs<\/p>\n
\n“`<\/p>\nX<\/code> array will contain the features of the dataset, and the y<\/code> array will contain the cluster labels.<\/p>\n","protected":false},"excerpt":{"rendered":"