I have a dataset that I have created in Python with a category called
change_label that has values "buy, hold, sell" (or alternatively I could use -1,0,1).
I am wondering, what is the best way to fit this data so that machine learning algorithms can be applied to it to make predictions?
My options are label encoding, label binzarization, or one-hot encoding. Basically, I thought that simply using sci-kit learn's
LabelBinarizer() would be enough. However, I want to be sure. Any advice?