I was reading through a notebook tutorial working with the Titanic dataset, linked here, and noticed that they highly favored ordinal data to continuous data.
For example, they converted both the Age and Fare features into ordinal data bins.
I understand that categorizing data like this is helpful when doing data analytics manually, as fewer categories makes data easier to understand from a human perspective. But intuitively, I would think that doing this would cause our data to lose precision, thus leading to our model losing precision or accuracy.
Can someone explain when is converting numerical data to ordinal data is appropriate, and the underlying statistics of why it is effective?