Description: Normalization parameters are the values used to normalize data in a dataset. Normalization is a crucial process in data preprocessing, especially in the fields of machine learning and data mining. This process involves adjusting the values of features in a dataset to have a common scale, which facilitates comparison and analysis. Normalization parameters can include measures such as mean and standard deviation, which are used to center and scale the data. For example, in Z-score normalization, the mean is subtracted from each value and divided by the standard deviation, resulting in a dataset with a mean of 0 and a standard deviation of 1. This is particularly important in algorithms that are sensitive to the scale of the data, such as k-nearest neighbors or logistic regression. Normalization not only improves model accuracy but also speeds up the convergence process during training. In summary, normalization parameters are essential to ensure that machine learning models operate effectively and efficiently, allowing for better interpretation and analysis of data.
