In count or frequency encoding, we replace the categories with the count or the percentage of observations with that category. That is, if 10 out of 100 observations show the category blue for the variable color, we would replace blue with 10 when doing count encoding, or by 0.1 if performing frequency encoding. These techniques, which capture the representation of each label in a dataset, are very popular in data science competitions. The assumption is that the number of observations per category is somewhat predictive of the target.
Note that if two different categories are present in the same percentage of observations, they will be replaced by the same value, which may lead to information loss.
In this recipe, we will perform count and frequency encoding using pandas and Feature-engine.
