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One Hot Encoding

One hot encoding is a process by which categorical variables are converted into a form that could be provided to ML algorithms to do a better job in prediction. Many machine learning libraries require class labels to be coded as integer values. Problem with label encoding is that it assumes higher the categorical value, better the category. This is why we use one hot encoder to perform “binarization” of the category and include it as a feature to train the model.

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Author of the review:
Spiros Sirmakessis
University of Peloponnese


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