As for many questions, the answer is "it depends":
- features which have a low individual importance may still add predictive power to your model, because the model benefits from combining their information together with information of other features.
- However they may introduce noise in the model and cause overfitting, thus decreasing the performance of the model.
The best way to answer your question is to experiment:
- order the features by decreasing importance
- loop from say 5 features to 30, each time selecting the top N features by importance, and training/testing a model based on this subset of features.
- plot the performance
You're likely to observe that the performance increases quite a lot at the start for each "important" feature added, then slows down as feature importance decreases and probably doesn't increase at all at some point, possibly even decreasing a bit.