L1-Normalisierung, auch bekannt als L1-Regularisierung or Lasso normalization, is a method used in various fields of maschinellem Lernen and Datenanalyse to scale data and verbessern. 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.
Die Formel für die L1-Normalisierung kann wie folgt ausgedrückt werden:
Hier ist 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 Merkmalsauswahl in hochdimensionalen Datensätzen.
Insgesamt spielt die L1-Normalisierung eine entscheidende Rolle bei der Vorbereitung für maschinelle Lernmodelle., ensuring that each feature contributes equally to the final outcome.