Binary Vectors
Binary vectors are a type of data structure used in data science and artificial intelligence to represent categorical data. They are composed of binary values, typically 0 or 1, that indicate the presence or absence of a particular category. Binary vectors are commonly used in machine learning algorithms, such as clustering and classification, to represent features or attributes of a dataset. They are particularly useful when dealing with large datasets that have many categorical variables, as they can be easily manipulated and compared using mathematical operations. Binary vectors can also be used to represent text data, where each binary value represents the presence or absence of a particular word in a document or corpus. In natural language processing, binary vectors are often used in conjunction with techniques such as bag-of-words and term frequency-inverse document frequency (TF-IDF) to extract meaningful features from text data.
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