methods

SCALE EMBEDDED CATEGORICAL DATA?
No, you shouldn't scale categorical data.
If the feature is categorical, it means that each value has a separate meaning, so normalizing will turn this features into something different.
There are several ways to deal with categorical data:
a) Integer Encoding: Where each unique label is mapped to an integer.
b) One Hot Encoding: Where each label is mapped to a binary vector.
c) Learned Embedding: Where a distributed representation of the categories is learned.
if the Target is continuous. Yes, you do need to scale the target variable if the target variable is having a large spread of values.

then you are saying the amount feature is lot lot better than the percent feature
and that's why usually one transform the data into the same distribution to not make a feature better than another feature

ensemble technique- extra tree classifier