Using coordinates for labelling? I was asked it was possible and to see for myself, I am trying to program it and read up more on it. I do not know what it is called or where to look but the general idea is as follows:

labels are converted into an N-dimensional space and trajectories are calculated along the N-dimensional space. Based on the direction, a label is assigned with a confidence interval.

The data

basic_data = [
  {"label":"First-Person RPG", "Tags":["Open-world", "Fantasy", "Adventure", "Single-player", "Exploration", "Dragons", "Crafting", "Magic", "Story-rich", "Moddable"]},
  {"label":"Action RPG", "Tags":["Open-world", "Fantasy", "Story-rich", "Adventure", "Single-player", "Monsters", "Crafting", "Horse-riding", "Magic", "Narrative"]},
  {"label":"Adventure", "Tags":["Difficult", "Dark Fantasy", "Action", "Single-player", "Exploration", "Lore-rich", "Combat", "Permadeath", "Monsters", "Atmospheric"]},
  {"label":"Party Game", "Tags":["Multiplayer", "Social Deduction", "Indie", "Strategy", "Casual", "Space", "Deception", "Survival", "Teams", "Interactive"]}
]

code for the first part below

mlb = MultiLabelBinarizer()

for idx, data in enumerate(basic_data):
    basic_data[idx]["tag_str"] = ",".join(data["Tags"])
    

pd_basic_data: pd.DataFrame = pd.DataFrame(basic_data)
tags: List = [str(pd_basic_data.loc[i,'tag_str']).split(',') for i in range(len(pd_basic_data))]

mlb_result = mlb.fit_transform(tags)
df_final: pd.DataFrame = pd.concat([pd_basic_data['label'],pd.DataFrame(mlb_result,columns=list(mlb.classes_))],axis=1)

a simple one word answer telling the theory works as well for an answer. I just need to know where to look.