Codificación de etiquetas is a technique used in preprocesamiento de datos, specifically for converting categorical data into a numerical format that aprendizaje automático algorithms can understand. It is particularly useful when dealing with categorical features that do not have an inherent order but need to be represented as numbers for entrenamiento del modelo.
In label encoding, each unique category value is assigned an integer value starting from 0. For example, if you have a variable categórica ‘Color’ with values [‘Red’, ‘Green’, ‘Blue’], label encoding would convert these to numerical values like:
- Rojo -> 0
- Verde -> 1
- Azul -> 2
Esta transformación sencilla permite que los algoritmos que dependen de entradas numéricas procesen eficazmente los datos categóricos.
However, it’s important to note that label encoding can introduce unintended ordinal relationships between categories. For instance, the model might mistakenly interpret ‘Red’ (0) as being less than ‘Green’ (1) and ‘Blue’ (2), which may not accurately reflect the nature of the data. To mitigate this issue, other encoding techniques like Codificación One-Hot might be used, particularly when the categorical variable is nominal (without a meaningful order).
En general, la codificación de etiquetas es un método sencillo para manejar datos categóricos, lo que la convierte en una opción común en diversos pipelines de aprendizaje automático donde existen características categóricas.