This is the fourth in a 4-part series where Anders Larson and Shea Parkes discuss predictive analytics with high cardinality features.  In the prior episodes we focused on approaches to handling individual high cardinality features, but these methods did not explicitly address feature interactions.  Factorization Machines can responsibly estimate all pairwise interactions, even when multiple high cardinality features are included.  With a healthy selection of high cardinality features, a well tuned Factorization Machine can produce results that are more accurate than any other learning algorithm.