Features engineering is the most important aspect of the NLP domain when you are trying to apply ML algorithms to solve your NLP problems. If you are able to derive good features, then you can have many advantages, which are as follows:
- Better features give you a lot of flexibility. Even if you choose a less optimal ML algorithm, you will get a good result. Good features provide you with the flexibility of choosing an algorithm; even if you choose a less complex model, you get good accuracy.
- If you choose good features, then even simple ML algorithms do well.
- Better features will lead you to better accuracy. You should spend more time on features engineering to generate the appropriate features for your dataset. If you derive the best and appropriate features, you have won most of the battle.
