One-Hot Encoding
One-Hot Encoding is a data preprocessing technique that converts categorical values into separate binary features, allowing machine learning models to work with categories represented as numerical values.
What is One-Hot Encoding?
One-Hot Encoding creates a separate feature for each possible category. For example, a Color feature with the values Red, Blue, and Green can be represented using three binary features. Each observation receives a value of 1 for its corresponding category and 0 for the others.
Why is One-Hot Encoding Important?
Many machine learning algorithms require numerical inputs and may incorrectly interpret categories as having an inherent numerical order. One-Hot Encoding represents categories without introducing such an ordering, making it useful for preparing categorical data for model training.
Common use cases
One-Hot Encoding is commonly used for categorical features in classification, regression, recommendation systems, natural language processing, and other machine learning applications.