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.
So now I am totally confused how people use one hot encoding for target variable in practice? please help me out.