Hey again Amro,

Notice how the n_samples parameter is implemented. The description in the documentation is as follows:
If int, it is the total number of points equally divided among clusters. If array-like, each element of the sequence indicates the number of samples per cluster.
Therefore, by passing 500 as an argument, you give the total number of points, which make_blobs then distributes (roughly) equally among 3 classes. Classes 0 and 1 have 167 representatives while class 2 has 166. You can convince yourself that this is the case by typing for example:

import numpy as np
np.count_nonzero(y_train == 1)

In order to perform the task from the exam successfully, you would need to pass an array as an argument which explicitly states the number of classes and the number of representatives from each class. We need 5 classes, with 100 representatives each. Therefore, the following would work:

n_samples = [100, 100, 100, 100, 100]

or, written in a more compact way:

n_samples = [100]*5

You would then need to do this analogously for the test dataset, this time having 50 representatives from each of the 5 classes. The rest of the parameters (n_features, random_state, and cluster_std) are defined correctly in your code.

Hope this helps!

Kind regards,
365 Hristina

Resolved: Course Exam Question 1 for Machine learning with KNN

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