MaxAbsskaalaus

MaxAbsskaalaus is a term encountered in some Finnish technical discussions to describe maximum absolute scaling, a data preprocessing technique used in statistics and machine learning. In English-language literature the method is commonly called maximum absolute scaling or MaxAbs scaling. The approach is closely related to the MaxAbsScaler in popular machine learning libraries and is often described as a simple method to normalize features while preserving their sign.

The procedure is straightforward. For each feature (or column) in a dataset, compute max_abs as the maximum of the absolute values across all samples: max_abs = max_i |x_i|. If max_abs is greater than zero, each value is scaled as x'_i = x_i / max_abs; if max_abs is zero, the corresponding feature is set to zero. The result is that the scaled features lie within the interval [-1, 1]. The scaling can be applied per feature, and for sparse data it can be implemented in a way that preserves sparsity.

MaxAbsskaalaus has several practical advantages: it is simple to implement, fast to compute, and preserves the sign of each feature, which can be desirable for certain models. It also ensures that all features share the same order of magnitude, which can help gradient-based learning algorithms. However, it is sensitive to outliers: a single large absolute value can compress all other values toward zero. It is common to use it in conjunction with cross-validation and to consider alternative scaling methods when outliers are present.