I want to learn from data where each record has a variable number of features that have no inherent order to them. Take as an example the task to predict whether a repair is worth it of some item. Each item can have a variable amount of features e.g. glass is broken, handle is missing etc. which are mostly categorical. There are up to +1000 different things to know about each item, but in reality there are only a handful present.
What ways are there to encode this kind of data or what models exist that can handle this natively?
I have a few things in mind
- Binary encoding of set (resulting in a lot of dimensions)
- Average features into single vector (dimensions become harder to interpret)
but I want to explore more options.