La normalización min-max es una técnica de preprocesamiento de datos used to scale numerical features to a specific range, usually between 0 and 1. This method transforms the original data points into a normalized scale, making it easier for aprendizaje automático algorithms to process the data effectively. The formula used for min-max normalization is:
Xnorm = (X – Xmin) / (Xmax – Xmin)
Donde:
- Xnorm is the valor normalizado.
- X es el valor original.
- Xmin is the minimum value in the dataset.
- Xmax es el valor máximo en el conjunto de datos.
By applying this transformation, the data is reshaped so that it fits within the desired range, which helps in reducing the effects of outliers and improving convergence during model training. Min-Max normalization is particularly useful for algorithms that are sensitive to the scale of data, such as neural networks and k-vecinos más cercanos.
However, it’s important to be aware that min-max normalization can be sensitive to outliers since they can significantly affect the minimum and maximum values. Therefore, it may be advisable to use other técnicas de normalización si el conjunto de datos contiene valores extremos.