Min-Max正規化

最小-最大正規化は データ前処理技術 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 機械学習 algorithms to process the data effectively. The formula used for min-max normalization is:

X_norm = (X – X_min) / (X_max – X_min)

ここで：

• X_norm is the 正規化された値.
• X これは元の値です。
• X_min is the minimum value in the dataset.
• X_max はデータセット内の最大値です。

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近傍法.

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 正規化手法 データセットに極端な値が含まれている場合。