One-hot encoding: Difference between revisions
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'''One-hot encoding''' encodes nominal categorical data into numerical ones by representing them as binary vectors. | '''One-hot encoding''' encodes nominal categorical data into numerical ones by representing them as binary vectors. The drawback is high-dimensional sparse data, which may be computationally expensive. | ||
[[Category: Machine Learning]] | [[Category: Machine Learning]] | ||
Latest revision as of 00:22, 1 May 2024
One-hot encoding encodes nominal categorical data into numerical ones by representing them as binary vectors. The drawback is high-dimensional sparse data, which may be computationally expensive.
