The learning process of a decision tree involves selecting appropriate features and splitting nodes based on criteria such as information gain, the information gain ratio, or the Gini index. These splitting criteria evaluate potential split points based on the relative ordering of feature values rather than their absolute magnitudes. Consequently, applying feature scaling or data normalization to the inputs does not affect the resulting splits or the tree structure, making such preprocessing unnecessary for decision tree models.