One-Hot Encoding
One-Hot Encoding is a data preprocessing technique that converts categorical variables (like "dog", "cat") into binary vector representations where only a single element is 1 (hot) and the rest are 0.
Frequently Asked Questions
Why use one-hot encoding?▼
Machine learning algorithms operate on numerical values. Converting words or categories to binary vectors allows them to process the data without implying a numerical ranking.
What is the curse of dimensionality in one-hot encoding?▼
If a category has thousands of unique values (e.g. city names), one-hot encoding creates massive, sparse vectors, wasting RAM.
Quick Facts
- CategoryMathematical Foundations
- Key ApplicationFeature engineering, input vector structuring, and tabular ML training data prep
Coverage Trend12 Weeks
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