Boosting techniques when having few features Are boosting techniques suitable only when having large number of features (covariates) or they can be beneficial with having for instance 5 or 6 features as well?  
 A: As commented above, there is no limitation in applying boosting when the number of features is small.
However, when the number of features is small:


*

*Boosting might not help or even cause damage

*You might not need boosting


Boosting might not help or even cause damage
The features you have might not have enough information about the concept. In such a case, any model (with or without boosting), will have a limited predictive ability. Using boosting might add more sub classifiers to your classifier, increasing its complexity and possibly lead to over fitting.
You might not need boosting
You mention 5 to 6 features. In case they are binary you have 32-64 options. In this case you might use brute force in order to explore all the options. This way you will be able to know for sure what is the best you can do with these features.
Please note that applying brute force is different from running trees with high depth or other complex enough models. While the results are likely to be the same in both cases, in the trees scenarios you won't know for sure that you have covered all the options.
