eloszlásvektor

Eloszlásvektor, which translates to "distribution vector" in English, is a concept used in certain statistical and machine learning contexts. It typically refers to a vector where each element represents a probability or a weight associated with a particular outcome, category, or component. In essence, it describes how a probability distribution is spread across a set of discrete possibilities.

For instance, in machine learning, when classifying an image into one of several categories (e.g., cat, dog, bird), a classification model might output an eloszlásvektor. If the input image is most likely a cat, the vector might look something like [0.8, 0.1, 0.1], where the first element corresponds to the probability of it being a cat, the second for a dog, and the third for a bird. The sum of the elements in a true probability distribution vector is always 1.

This concept is also relevant in areas like topic modeling, where an eloszlásvektor can represent the distribution of topics within a document, or in recommender systems, where it might describe a user's preference distribution across different item categories. The term emphasizes the "spreading out" or distribution of a quantity across different dimensions or possibilities, as captured by the vector's components.