Ilisarnaat modeli saqqummersuutigineqarnera pillugu, taaneqarsinnaanngitsuuffik seqvesterineqarfik-digit-taarneri aningaasat odd'imik nalunaarusuk tunngavigalugu
[2, 4, 6, 8]
Tassa allaffeqarfiuvoq qalipaataavoq, sumiiffiat innuttaasut ullorianut naaperungaanni najugaqarnera, taaguuni qanoq kingorna atorfiit eqqarsaatitaavut ullorianut nalunaarsuineq. Ullorianut qalipaat qimmeqarneq Pythonimi naapinneqarpoq, NumPy, scikit-learn-imik ilinniartitsisut. 

```python
import numpy as np
from sklearn.naive_bayes import GaussianNB

# Oqaaseq aallartitsisunneqarfik qanoq kingorna atorfiit eqqarsaatitaavut
def digits_to_array(digits):
    result = []
    for digit in digits:
        row = [0] * 10
        row[digit] = 1
        result.append(row)
    return np.array(result)

# Oqaaseq aallartitsisunneqarfik ullorianut qanorluunneqarpoq
def train_model(X, y):
    classifier = GaussianNB()
    classifier.fit(X, y)
    return classifier

# Oqaaseq aallartitsisunneqarfik ullorianut siunnersuutigineqarpoq
def predict_output(classifier, digits):
    X = digits_to_array(digits)
    y_pred = classifier.predict(X)
    return y_pred

# Aallartitsisunneqarfiuvoq qanorluunneqarpoq
X_train = digits_to_array([2, 4, 6, 8])
y_train = np.array([0, 0, 0, 0])  

# Ullorianut qanorluunneqarpoq
classifier = train_model(X_train, y_train)

# Ullorianut siunnersuutigineqarpoq
digits = [1, 3, 5, 7]
output = predict_output(classifier, digits)
print(output)  
```