Label Encoding
Label Encoding is a preprocessing technique in machine learning that converts categorical text labels into numerical values, typically integers, to make them usable by algorithms that require numerical input. It assigns a unique integer to each distinct category, such as mapping 'red', 'green', 'blue' to 0, 1, 2, but it does not imply any ordinal relationship between the categories. This method is commonly used for nominal categorical variables where the order does not matter, but it can introduce unintended ordinal assumptions if misapplied.
Developers should use Label Encoding when working with machine learning models like decision trees, random forests, or gradient boosting that can handle integer-encoded categorical features efficiently, especially for nominal data with no inherent order. It is particularly useful in scenarios with high-cardinality categorical variables where one-hot encoding would create too many sparse features, helping to reduce dimensionality and computational cost. However, it should be avoided for ordinal data or when using algorithms sensitive to numerical magnitude, as the arbitrary integer assignments might mislead the model.