I've been searching around for a while without any luck - hopefully someone more knowledgable can give me some advice about the following ML problem that I've been thinking about:
Say you are trying to predict the Rotten Tomato "Tomatometer" score of a film before it's released. Typically you might approach this by compiling a list of features and labels for some existing films and input this into a supervised ML algorithm.
In this example, the feature list will be standard metrics that describe the film such as, budget, filming duration, number of actors, etc., while the label is the Tomatometer score of the film that is given as a value between 0 to 100. Every film can be expressed using this score, but individually they are spread across many genres, country of production, etc. meaning that there are natural subsets within the training data.
Let's say our training data contains only films belonging to five genres (e.g. Action, Thriller, Horror, Fantasy and Documentary), while we want our algorithm to be applicable to films outside of this genre (e.g. Sci-Fi or Animation), but for the sake of question, we do not have access to these entire categories. In this example, were also assuming that some features will be more important to certain genres than others, for example, a large cast size may correlate more with the score for Action films than for Animations.
What is the general way to transform the data to make it invariant to the subgroup (genre), or what ML algorithm can be used here (if any)? Is there a common name for this situation (some keywords I can search?)