Minmaxskaala

Minmaxskaala, also known as min‑max scaling, is a linear transformation that rescales data to a fixed range, typically [0, 1] or [−1, 1]. Given a set of values xi, the transformed value xi′ is calculated as

xi′ = (xi − min(X))/(max(X) − min(X))

which maps the minimum and maximum of the original data to the chosen bounds. The method preserves the relative distribution of the data while ensuring that all values lie within the defined interval.

In statistical analysis and data preprocessing for machine learning, Minmaxskaala is frequently applied to features before feeding them into algorithms that are sensitive to the scale of input data, such as k‑means clustering, support‑vector machines, and neural networks. It is particularly useful when the data is already bounded and outliers are not a significant concern.

A key advantage of Minmaxskaala is its simplicity and interpretability; the transformed values are directly comparable across features that share the same scale. However, the technique can be heavily influenced by extreme values: outliers can compress the majority of data points into a narrow range, potentially degrading model performance. Alternatives such as z‑score standardization or robust scaling mitigate this sensitivity by focusing on central tendency and dispersion.

Minmaxskaala is thus a foundational preprocessing step in many data‑centric workflows, offering a straightforward means of normalizing disparate measurements for consistent analysis.