minmaxnormeerimist

Minmaxnormeerimist, often referred to as min-max normalization or feature scaling, is a data preprocessing technique used in machine learning and statistics. Its primary goal is to rescale features to a fixed range, typically between 0 and 1, or sometimes -1 and 1. This transformation is beneficial for algorithms that are sensitive to the scale of input features, such as gradient descent-based algorithms, support vector machines, and k-nearest neighbors.

The min-max normalization formula for a given feature is calculated as: X_normalized = (X - X_min) / (X_max - X_min), where X is the original feature value, X_min is the minimum value of the feature across all data points, and X_max is the maximum value of the feature. If the desired range is [-1, 1], a further scaling and shifting step is applied.

The key advantage of min-max normalization is that it preserves the relationships among the original data values. However, it is sensitive to outliers. If a dataset contains extreme values, these outliers can disproportionately affect the calculated X_min and X_max, leading to a compressed range for the majority of the data. Therefore, it is often recommended to handle outliers before applying min-max normalization. An alternative scaling method that is more robust to outliers is standardization.