normaliseerun

Normaliseerun is a term used in data processing and statistics to describe the process of normalization, the adjustment of data values to a common scale or distribution in order to enable comparability across features, samples, or sources. The goal is to reduce the impact of differing units and scales so that analyses and models can treat variables on a similar footing.

Common methods include min–max normalization, which rescales values to a fixed range such as 0 to 1; z-score normalization, which centers data around the mean and scales by the standard deviation; decimal scaling, which reduces the magnitude by dividing by a power of 10; and log transformations, which reduce skew and stabilize variance.

These techniques are widely used in data preprocessing for machine learning, sensor data fusion, image and audio processing, and cross-site data integration, where disparate units or distributions must be reconciled. The choice of method depends on data characteristics and the downstream task. Min–max preserves relationships but is sensitive to outliers; z-score assumes roughly normal distribution and maintains the original shape; log transformations require positive values and can improve normality.

Limitations include potential distortion of interpretability after transformation, the need to back-transform results for reporting, and the risk of introducing artefacts if the normalization is not applied consistently across training and test data. See also normalization, standardization, and data preprocessing.