Optimizing model-agnostic Random Subspace ensembles

This paper presents a model-agnostic ensemble approach for supervised learning. The proposed approach is based on a parametric version of Random Subspace, in which each base model is learned from a feature subset sampled according to a Bernoulli distribution. Parameter optimization is performed using gradient descent and is rendered tractable by using an importance sampling approach that circumvents frequent re-training of the base models after each gradient descent step. While the degree of randomization is controlled by a hyper-parameter in standard Random Subspace, it has the advantage to be automatically tuned in our parametric version. Furthermore, model-agnostic feature importance scores can be easily derived from the trained ensemble model.