Minmaxskaling

Minmaxskaling is a term sometimes used to refer to min-max scaling, a data preprocessing technique that normalizes numerical features to a fixed range, typically 0 to 1. The method computes the minimum and maximum values of each feature in the training data and rescales each observation x according to x' = (x − x_min) / (x_max − x_min). If x_max equals x_min for a feature, that feature can be set to 0 for all instances to avoid division by zero.

The rescaled features preserve the order of values and the relative proportions of differences within each feature, but they are sensitive to outliers because extreme values determine the range. As a result, features with outliers can be disproportionately stretched or compressed, affecting downstream models.

Min-max scaling is commonly used in machine learning algorithms that rely on distance or gradient-based optimization, such as k-nearest neighbors, support vector machines with certain kernels, and neural networks, where scaled features can improve convergence and performance. In practice, the transformation is applied per feature: compute the min and max on the training data, transform the training set, and apply the same parameters to validation and test sets to avoid data leakage.

For new data, use the same min and max parameters to maintain consistency; some workflows re-fit the scaler on new data, but this can shift feature distributions and should be done with care. The range can be customized, for example to [-1, 1], in many library implementations. Alternatives include standardization (z-score) and robust scaling, which reduce sensitivity to outliers.

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