I have a fake news detection problem and it predicts the binary labels "1"&"0" by vectorizing the 'tweet' column, I use three different models for detection but I want to use the ensemble method to increase the accuracy but they use different vectorezer.
I have 3 KNN models the first and the second one vectorizes the 'tweet' column using TF-IDF.
from sklearn.feature_extraction.text import TfidfVectorizer vector = TfidfVectorizer(max_features =5000, ngram_range=(1,3)) X_train = vector.fit_transform(X_train['tweet']).toarray() X_test = vector.fit_transform(X_test['tweet']).toarray()
for the third model I used fastText for sentence vectorization
%%time sent_vec =  for index, row in X_train.iterrows(): sent_vec.append(avg_feature_vector(row['tweet'])) %%time sent_vec1 =  for index, row in X_test.iterrows(): sent_vec1.append(avg_feature_vector(row['tweet']))
after scaling and... my third model fits the input like this
scaler.fit(sent_vec) scaled_X_train= scaler.transform(sent_vec) scaled_X_test= scaler.transform(sent_vec1) . . . knn_model1.fit(scaled_X_train, y_train)
now I want to combine the three models like this and I want the ensemble method to give me the majority just like
VotingClassifier, but I have no idea how can I deal with the different inputs (TF-IDF & fastText) is there another way to do that?