High number of unique values and tree based models

Having columns of data with high cardinality can adversely affect the performance of your models. The idea of this article stemmed from my personal experience of employing tree based solutions in various projects. In this article I will attempt to show the effects of this on a couple of datasets using the simple decision tree. Cardinality can be defined as the uniqueness of data in the machine learning context. Examples of fields with a high number of unique values include cities, countries, medical diagnosis codes, movie categories on Netflix, flavours of ice cream, etc.