Z-Score Normalization
Z-Score Normalization is a data preprocessing technique that transforms numerical values so they are centered around a mean of zero and scaled according to their standard deviation.
What is Z-Score Normalization?
Z-score normalization calculates how many standard deviations an individual value is away from the dataset mean. The resulting standardized values typically have a mean of 0 and a standard deviation of 1.
Why is Z-Score Normalization Important?
Z-score normalization puts features with different scales onto a common scale, preventing variables with larger numerical ranges from disproportionately influencing a model. It can also improve the stability and convergence of many machine learning algorithms.
Common use cases
Z-score normalization is commonly used in feature preprocessing, statistical analysis, anomaly detection, clustering, regression, classification, and machine learning models that are sensitive to feature scale.