AN APPROACH TO FEATURE SPACE FORMATION FOR MACHINE LEARNING MODELS
DOI:
https://doi.org/10.47390/ts-v3i9y2025No2Keywords:
One-Hot Encoding, Label Encoding, Binning, Empirical Rule, MinMax Normalization, z-Normalization, StandardScaler, Normalization, Scaling.Abstract
In this study, various approaches to feature space formation for solving data processing tasks are examined, and their implementation mechanisms in a programming environment are investigated. The paper describes methods for establishing internal relationships based on data characteristics, categorizing information, and handling categorical variables. Furthermore, techniques for grouping, scaling, and normalizing numerical data are presented, including practical approaches to data preprocessing in software environments.
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