L1正規化は、別名 L1正則化 or Lasso normalization, is a method used in various fields of 機械学習 and データ分析 to scale data and モデルの性能を向上させるために. 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.
L1正規化の式は次のように表されます：
ここで、 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 特徴選択 高次元データセットにおいて
全体として、L1正規化は 使用される, ensuring that each feature contributes equally to the final outcome.