One-hot encoding transforms categorical data into binary vectors that machine learning models can process. Each category becomes a separate column with 1 indicating presence and 0 indicating absence, preventing models from assuming false ordinal relationships between categories. The technique is essential for nominal data like
Table of contents
Key TakeawaysWhat is One-Hot Encoding?Why Use One-Hot Encoding?What is Label Encoding?One-Hot Encoding vs Label EncodingHow to Perform One-Hot Encoding (with Python Examples)When Not to Use One-Hot EncodingHandling One-Hot Encoded Data EfficientlyPractical Example: One-Hot Encoding in a Classification ModelFAQ’sConclusionReferencesSort: