Would a machine learning classifier algorithm be able to determine whether a number is odd or even? I was testing out some classifier algorithms in scikit but wasn't able to find a classifier (linear or non-linear) that managed to provide good prediction on whether an input number is odd or even. Here's the code:
import numpy as np
from sklearn.linear_model import SGDClassifier
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV, RidgeClassifierCV
from sklearn.svm import SVC, NuSVC
length = 10000

evens = np.array([np.arange(0, length*2, 2)])
zeroes = np.array([np.resize([[0]], length)])

odds = np.array([np.arange(1, length*2, 2)])
ones = np.array([np.resize([[1]], length)])

evens_data = np.concatenate((evens.T, zeroes.T), axis=1)
odds_data = np.concatenate((odds.T, ones.T), axis=1)

data = np.array([np.append(evens_data, odds_data)]).reshape(length*2, 2)
np.random.shuffle(data)
X = np.array([data[:,0]]).reshape(length*2,1)
y = data[:,1]

## 
## Tried all algorithms below
##
# clf = LogisticRegression(random_state=42, max_iter=1000)
# clf = LogisticRegressionCV(cv=5, random_state=42, max_iter=1000)
# clf = RidgeClassifierCV(cv=5)
# clf = SGDClassifier(max_iter=1000)
# clf = SVC(random_state=42, max_iter=1000)
clf = NuSVC(random_state=42)
clf.fit(X, y)

for tuple_ in [(np.arange(0, 20, 2), 0), (np.arange(1, 22, 2), 1)]:
    X, y = tuple_
    for x in X:
        predicted = clf.predict([[x]])
        print(f'{"Good" if predicted[0] == y else "Bad"} predicting: {x} is {predicted[0]} and it should be {y}')

I anticipate some people saying that I could (should) have processed my data in a more machine learning ready way (like translating it to binary) but I didn't want to do that as my idea is to being able to find a classifier algorithm that could clusterize the data based on features not as clear as odd/even but on something the analyst couldn't foresee just by looking at the data.
I wonder if Linear or SVM-based classifier algorithms aren't able to do that. If not, I'd appreciate someone explaining why not.
Also, (if Linear / SVM-based classifiers are not indicated for this job) what would be the right tool for that?

I think a linear model can't do it because there is no direct or inverse relationship between a number (your feature) and the class it belongs to (odd/even). I mean, you can't say something like "as the number gets bigger it will tend to be odd". Also, there are no clusters of even and odd numbers, both classes are uniformly distributed in the set of integer numbers, so SVM, KNN or any clustering algorithm will fail as well.
I think you should ask yourself the following: "How can I recognize whether a number is even or odd if the only thing I know is the number itself?" well, there are a bunch of ways to do that. For example, you can divide by two and check if the result is an integer or decimal, but linear models (or any ML model, I think) know nothing about integers or decimal numbers, so they can't help you. Other ways would be using transformations as described in the previous answers but, again, a linear model won't be able to create a binary or sine transformation (perhaps a deep ANN can do something like the sine transform, but I'm not sure).
At the end, I think the answer was already given:

"...the issue is one of defining your features".

You need to help the ML model by providing useful features.
