title: How to use one hot encoding for multiple label(trainy) in .fit() method?
body: <p>I have a mobile price classification dataset in which I have 20 features and one target variable called price_range. I need to classify mobile prices as low, medium, high, very high. I have applied a one-hot encoding to my target variable. After that, I split the data into trainX, testX, trainy, testy. So my shape for trainX and trainy is (1600,20) and (1600,4) respectively.
  Now when I try to fit trainX and trainy to logisticRegresion,
  i.e -> lr.fit(trainX,trainy)
  I am getting an error and it says: bad input (1600,4)
  So, I understood that I have to give trainy value in shape (1600,1)
  but by one hot encoding I have got array of 4 columns for each individual price_range as per the concept of one hot encoding.</p>
  
  <p>So now I am totally confused how people use one hot encoding for target variable in practice?
  please help me out.</p>