I am trying to fit a hypothesis to my data in order to predict the duration for a certain event.
My data is strictly categorical and I used one-hot encoding for all features.
After using one-hot encoding the dataset now has the dimensions 20.000x414.
First I tried linear regression with regular gradient descent. However, both training and cross validation error is large.
I concluded that this is a case of high bias (underfitting).
Now I want to try a polynomial of higher order but I think that it would not make any difference because all of the features are either '1' or '0'.
If I use the square or the cube of those features they will still be '1' or '0', right?
Is there something else I can try in order to deal with the high bias?