Onehotvasteet

Onehotvasteet is a term that appears in discussions related to machine learning and data preprocessing. It translates roughly to "one-hot encoding" in English, a technique used to convert categorical variables into a numerical format that machine learning algorithms can understand.

Categorical variables represent types or labels, such as colors (red, blue, green) or cities (London, Paris, Tokyo). Many machine learning algorithms require numerical input, so these categorical labels must be transformed. One-hot encoding achieves this by creating new binary columns for each unique category. For a given data point, the column corresponding to its category will have a value of 1, while all other newly created columns will have a value of 0.

For example, if a feature "color" has categories "red," "blue," and "green," one-hot encoding would transform a data point with the color "blue" into a representation where a "blue" column is 1 and "red" and "green" columns are 0. This prevents algorithms from incorrectly inferring an ordinal relationship between categories, which is a common issue when simply assigning arbitrary numbers. The term "onehotvasteet" refers to this process and its resulting numerical representation.

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