Znormalization

Z-normalization, also known as standardization or Z-score normalization, is a data preprocessing technique used in machine learning and statistics. It transforms data by scaling it so that its mean is 0 and its standard deviation is 1. This process is achieved by subtracting the mean of the data from each data point and then dividing the result by the standard deviation. The formula for Z-normalization is: z = (x - μ) / σ, where x is the original data point, μ is the mean of the dataset, and σ is the standard deviation of the dataset.

This technique is particularly useful when algorithms are sensitive to the scale of input features, such as support vector machines (SVMs), logistic regression, and principal component analysis (PCA). By centering the data around zero and setting the variance to one, Z-normalization ensures that no single feature dominates the others due to its magnitude. It helps to prevent features with larger values from unfairly influencing the model's learning process. Additionally, it is often a prerequisite for algorithms that assume normally distributed data. While Z-normalization is effective, it assumes that the data is approximately normally distributed. If the data significantly deviates from a normal distribution, other normalization methods like Min-Max scaling might be more appropriate. It is also sensitive to outliers, as they can disproportionately affect the mean and standard deviation.