Here is one row from my data:
H 7.042 5.781 5.399 -9.118 5.488 7.470
The first column is a categorical class. The rest of them are continuous numerical features.
I encoded the classes using one-hot-encoding labels and concatenated them with the numerical features list:
1 0 1 7.042 5.781 5.399 -9.118 5.488 7.470
Then I used this list for training.
- Is this a valid technique?
- Am I achieving anything really useful here?
My supervisor says that I made a mistake by using labels as features. I am trying to understand what I did wrong. In my view, the mistake seems to be: when I am using labels as features, I must provide those labels as input whenever I want to use the trained model. Therefore, this trained model is practically useless. Am I correct?
- Cross posted in stats.SE