Binary Encoding
Binary encoding is a technique used in data science and artificial intelligence to represent categorical data as binary vectors. In this technique, each category is assigned a unique binary code, and each observation is represented as a vector of binary values, where each element corresponds to a category and is set to 1 if the observation belongs to that category, and 0 otherwise. Binary encoding is useful in machine learning algorithms that require numerical input, as it allows categorical data to be represented in a way that can be easily processed by these algorithms. It also reduces the dimensionality of the data, as each category is represented by a fixed number of binary values, regardless of the number of categories in the dataset.
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