ValueError: Found input variables with inconsistent numbers of samples: [2004, 2005] when I try to fit my model

Question

Im trying to fit my model however I keep getting the following error:

 y = column_or_1d(y, warn=True)
Traceback (most recent call last):
  File "/Users/amanpuranik/PycharmProjects/covid/fake news 2.py", line 107, in <module>
    model.fit(x_train,y_test)
  File "/Users/amanpuranik/PycharmProjects/covid/venv/lib/python3.7/site-packages/sklearn/naive_bayes.py", line 609, in fit
    X, y = self._check_X_y(X, y)
  File "/Users/amanpuranik/PycharmProjects/covid/venv/lib/python3.7/site-packages/sklearn/naive_bayes.py", line 475, in _check_X_y
    return check_X_y(X, y, accept_sparse='csr')
  File "/Users/amanpuranik/PycharmProjects/covid/venv/lib/python3.7/site-packages/sklearn/utils/validation.py", line 765, in check_X_y
    check_consistent_length(X, y)
  File "/Users/amanpuranik/PycharmProjects/covid/venv/lib/python3.7/site-packages/sklearn/utils/validation.py", line 212, in check_consistent_length
    " samples: %r" % [int(l) for l in lengths])
ValueError: Found input variables with inconsistent numbers of samples: [3207, 802]

from my understanding, this means that when I fit my x and my y, they are different shapes. However when I print out their shapes they are both the same:

(4009, 1)
(4009, 1)

So im not sure why I am getting this error. What can I do to fix this? Here is my code:

data = pd.read_csv("/Users/amanpuranik/Desktop/fake-news-detection/data.csv")
data = data[['Headline', "Label"]]

x = np.array(data['Headline'])
y = np.array(data["Label"])
    #lowercase
lower = [[word.lower() for word in headline] for headline in stemmed2] #start here

#conver lower into a list of strings
lower_sentences = [" ".join(x) for x in lower]
print(lower_sentences)

#organising
articles = []


for headline in lower:
    articles.append(headline)

#print(articles[0])

#creating the bag of words model

headline_bow = CountVectorizer()
headline_bow.fit(lower_sentences)
a = headline_bow.transform(lower_sentences)
print(a)
b = headline_bow.get_feature_names()

#testing and training part
yy = np.reshape(y,(-1,1))
lower2 = np.reshape(lower_sentences,(-1,1))
x_train, x_test, y_train, y_test = train_test_split(lower2, yy, test_size=0.2, random_state=1)

print(lower2.shape)
print(yy.shape)



#fitting on the model now

model = MultinomialNB() #don forget these brackets here
model.fit(x_train,y_test) #this is where the error comes in 

Answer 1

I think, it should be y = [column_or_1d(y, warn=True)] instead of y = column_or_1d(y, warn=True)

Answer 2

Change model.fit(x_train,y_test) to model.fit(x_train,y_train). I don't know if that fixes the error, but its wrong. You don't fit with both train and test data that doesn't match.