Random Forests offer a large range of advantages, and they are deemed the first algorithm you should try on your data to figure out what kind of results can be obtained. This is because the Random Forests do not have too many hyperparameters to be fixed, and they work perfectly fine out of the box. They can naturally work with multiclass problems. Moreover, Random Forests offer a way to estimate the importance of variables for your insight or feature selection, and they help in estimating the similarity between the examples since similar cases should end up in the same terminal leaves of many trees of the ensemble.
However, in classification problems, the algorithm lacks the capability of predicting ...
