Features selection/combination for random forest
The Random Forest model in sklearn has a feature_importances_ attribute to tell you which features are most important. Here is a helpful example.

There are a few other algorithms for selecting the best features that generalize to other models such as sequential backward selection and sequential forward selection. In the case of sequential forward selection, you begin by finding the single feature that provides you with the best accuracy. Then, you find the next feature in combination with the first that gives you the best accuracy. This pattern continues until you find $k$ features, where $k$ is the number of features you want to use. Sequential backward selection is just the opposite, where you start with all of the features and remove those which inhibit your accuracy the most. You can find more information on these algorithms here.