Due to the fact that only one index has a non-zero value, this encoding type is known as a one-hot encoding. More frequently, our vector could include word counts for a larger text segment. This illustration is referred to as a "bag of words."
The bag-of-words model is a condensing representation being used information retrieval and natural language processing.
This paradigm ignores syntax and even word order while maintaining multiplicity and represents a text (such as a sentence or document) as the bag of its words.
One hot encoding, that is a crucial step in transforming categorical data variables for use by machine and deep learning algorithms, enhances a model's categorization and forecasting accuracy.
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