Feature Engineering
It is also called feature extraction
Purpose: Turn preprocessed text into → numeric vector (i.e. text representation?) that can be fed into ML
Feature engineering in ML VS in DL
In ML:
Pros:
We handcraft the function to do this.
i.e. it remains interpretable, we can explain the feature correlation
Cons:
Handcrafted function is a bottleneck sometimes. It affects the performance
Also, the wrong choice of feature could lead to big harm on the model.
In DL:
Pros:
Pre-processed raw data is directly fed into DL model.
So the model performance is higher
DL model “learn” the features from data.
Cons:
HOWEVER, it learns all features from data, it is hard to tell the correlation then.
i.e. it loses interpretability.