this isn't modifying the existing variable, it's allocating a new chunk of memory, and the nameweights refers to that new chunk. So the original data passed to this function is never changed. Therefore this change in this PR is unnecessary.

I did not intend to modify the variable weights passed by the user this is why I created new variable weights_

You won't be changing the original variable, that's now how python works 😉

Investigate this example:

import numpy as np

a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

def f(a):
    a = a + 1
    return a

print(f(a))
print(a)

this isn't modifying the existing variable, it's allocating a new chunk of memory,

Thank you @adrinjalali for the correction. make_classification returns a bunch object which also contains a dictionary of the original value of parameters like n_samples, n_features and weights that was used to generate the data.

https://github.com/SuccessMoses/scikit-learn/blob/test/sklearn/datasets/_samples_generator.py#L356-L383

reassigning the variable weights will change its content. Is there a way to work around this?

I see, I'd missed that