One-hot encoding
One-hot encoding is a technique used to convert categorical data into a binary matrix (1s and 0s). Each category is transformed into a new column, and a 1 is placed in the column corresponding to the category present for each observation, while all other columns get a 0. This method is particularly useful when dealing with categorical data where there is no ordinal relationship among categories.
When to use one-hot encoding
One-hot encoding is suitable for categorical data that lacks a natural order or ranking among categories. Here are some scenarios where it is appropriate:
- Nominal categorical data: When dealing with nominal data, where categories are distinct and have no inherent order.
- Algorithms that don’t handle ordinal data: Some ML algorithms (for example, decision trees and random forests) are not designed to handle ordinal data correctly. One-hot encoding ensures that each category is treated as a separate entity.
- Preventing misinterpretation...
