{"id":478260,"date":"2023-08-09T09:29:53","date_gmt":"2023-08-09T09:29:53","guid":{"rendered":""},"modified":"2023-09-05T11:16:22","modified_gmt":"2023-09-05T11:16:22","slug":"one-hot-encoding","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/de\/wiki\/one-hot-encoding\/","title":{"rendered":"One-Hot-Kodierung"},"content":{"rendered":"

One-hot encoding is a process by which categorical variables are converted into a numerical format that can be fed into machine learning algorithms. In this method, each unique category in a particular feature is represented by a binary vector.<\/p>\n

The History of the Origin of One-Hot Encoding and the First Mention of It<\/h2>\n

The concept of one-hot encoding dates back to the early days of computer science and digital logic design. It was widely used in the implementation of finite state machines in the 1960s and 70s. In machine learning, one-hot encoding started to become popular in the 1980s with the rise of neural networks and the need to handle categorical data.<\/p>\n

Detailed Information about One-Hot Encoding. Expanding the Topic One-Hot Encoding<\/h2>\n

One-hot encoding is employed to handle categorical data, which is common in many types of datasets. Traditional numerical algorithms require numerical input, and one-hot encoding assists in converting categories into a form that can be provided to machine learning models.<\/p>\n

Process<\/h3>\n
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  1. Identify the unique categories in the data.<\/li>\n
  2. Assign a unique integer to each category.<\/li>\n
  3. Convert each unique integer to a binary vector where only one bit is ‘hot’ (i.e., set to 1) and the rest are ‘cold’ (i.e., set to 0).<\/li>\n<\/ol>\n

    Example<\/h3>\n

    For a feature with three categories: “Apple,” “Banana,” and “Cherry,” the one-hot encoding would look like:<\/p>\n