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By scaling features, we can prevent one feature from dominating the decisions of a model. For example, say heights (cm), and age (years) are two features in my data. Since range of heights is larger than of years, a trained model could weight importance of heights much more than years. This could result in a poor model in return.
However, say that all of my features are binary, they take a value of either 0 or 1. In such a case, does feature scaling still have any benefits?
If all you features are binary, then, you don't need to apply normalization on them. Since their values are on the same scale already.
