[amp_mcq option1=”make_blobs” option2=”random_state” option3=”test_size” option4=”training_size” correct=”option2″]<\/p>\n
The correct answer is: B. random_state<\/strong><\/p>\n make_blobs<\/strong> is a function in the scikit-learn library that creates a dataset of points that are clustered into a specified number of clusters. The function takes a number of parameters, including the number of clusters, the number of points in each cluster, and the standard deviation of the points in each cluster.<\/p>\n random_state<\/strong> is a parameter that can be used to control the randomness of the data generation process. If random_state is set to a NumPy RandomState generator or an integer seed, then the data will be generated using the same random number sequence each time the function is called. This can be useful for debugging or for ensuring that the results of the function are reproducible.<\/p>\n test_size<\/strong> and training_size<\/strong> are parameters that control the size of the training and test sets. The training set is used to train the model, and the test set is used to evaluate the model’s performance. The default values for test_size and training_size are 0.2 and 0.8, respectively. This means that 20% of the data will be used for the test set and 80% of the data will be used for the training set.<\/p>\n Here is an example of how to use the make_blobs function to create a dataset of 100 points that are clustered into 3 clusters:<\/p>\n “` X, y = make_blobs(n_samples=100, n_features=2, centers=3, cluster_std=1.0) The output of the make_blobs function is a tuple of two NumPy arrays: X and y. X is a 2D array that contains the data points, and y is a 1D array that contains the cluster labels for each data point.<\/p>\n The following code shows how to plot the data points:<\/p>\n “` plt.scatter(X[:, 0], X[:, 1], c=y, s=50) The output of the code is a scatter plot of the data points, with each cluster colored differently.<\/p>\n I hope this helps! Let me know if you have any other questions.<\/p>\n","protected":false},"excerpt":{"rendered":" [amp_mcq option1=”make_blobs” option2=”random_state” option3=”test_size” option4=”training_size” correct=”option2″]<\/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-47512","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n
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\nfrom sklearn.datasets import make_blobs<\/p>\n
\n“`<\/p>\n
\nimport matplotlib.pyplot as plt<\/p>\n
\nplt.show()
\n“`<\/p>\n
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