This is the third in a 4-part series where Anders Larson and Shea Parkes discuss predictive analytics with high cardinality features.  In this episode they focus on feature engineering via the hashing trick.  The hashing trick is most applicable for extremely high cardinality, and at first glance can seem almost ridiculous.  In a lot of ways, it is the same as bucketing values at random.  But there are times that it is more valuable to include randomly engineered buckets than to exclude the original high cardinality feature entirely.