Data standardization and normalization

• In the data preprocessing stage, the Parrot platform will standardize and normalize the collected data to ensure that the data has a unified scale and range and improve the training effect and stability of the model.

  1. Data normalization: Data normalization is scaling the data so that it falls into a specific range. Common standardization methods include Z-score standardization and Min-Max standardization.

     • Z-score standardization: subtract the mean from the data and then divide it by the standard deviation so that the standardized data has a mean of 0 and a standard deviation of 1. The formula is as follows:

       [ x_{\text{Normalized}} = \frac{x - \mu}{\sigma} ]

     • Min-Max normalization: Linearly scale the data to a specific range, usually [ 0, 1 ] or [ -1, 1 ] . The formula is as follows:

       [ x_{\text{Normalized}} = \frac{x - \min(x)}{\max(x) - \min(x)} ]

  2. Data normalization: Data normalization is to scale the data to a specific interval, usually [ 0, 1 ] or [ -1, 1 ] . Common normalization methods include Min-Max normalization and Z-score normalization.

     • Min-Max normalization: Linearly scale the data to a specific range, usually [ 0, 1 ] or [ -1, 1 ] . The formula is as follows:

       [ x_{\text{Normalization}} = \frac{x - \min(x)}{\max(x) - \min(x)} ]

     • Z-score normalization: Subtract the mean from the data and then divide it by the standard deviation so that the normalized data has a mean of 0 and a standard deviation of 1. The formula is as follows:

       [ x_{\text{Normalization}} = \frac{x - \mu}{\sigma} ]

  Through data standardization and normalization processing, the dimensional differences between different features can be eliminated, the convergence speed and stability of the model can be improved, and the generalization ability of the model can also be improved.