How to decide to go with BOW or TFIDF I know that there are methods that help in selecting features such as Matual Info, and Info Gain, etc.
But for datasets with thousands of records and thousands of features it is time consuming to train the model in BOW and TFIDF to decide which method is better.
is there a way to decide which method to choose without the need to spend all this time?
It depends on the problem you are trying to solve. If you know the signal in the dataset already, the words which decide your decision then go with Bag of Words. This is useful when you are doing something like text classification.
On the other hand, TF-IDF is useful when you don't know the signal in the dataset. If you want to do text similarity, then, this is a good option.