La Normalización L1, también conocida como Regularización L1 or Lasso normalization, is a method used in various fields of aprendizaje automático and análisis de datos to scale data and mejorar el rendimiento del modelo. The primary goal of L1 Normalization is to adjust the values of the data points such that their total absolute value equals one. This is achieved by dividing each individual value by the sum of the absolute values of all data points in the dataset.
La fórmula para la normalización L1 puede expresarse como:
Aquí, x represents an individual data point, and the denominator is the sum of the absolute values of all data points in the dataset. This process ensures that the transformed data will be bounded within the range of -1 to 1, leading to a more uniform distribution of values.
L1 Normalization is particularly useful in scenarios where the data may have varying scales or units, as it helps to eliminate biases that might arise from such differences. It is commonly used in algorithms such as Lasso regression, where it encourages sparsity in the model by shrinking some coefficients to zero. This characteristic makes L1 Normalization a valuable technique for selección de características en conjuntos de datos de alta dimensión.
En general, la Normalización L1 juega un papel crucial en la preparación de para modelos de aprendizaje automático, ensuring that each feature contributes equally to the final outcome.