# Tag Info

4

Rescaling the input features is just a linear transformation. There's no right or wrong way of rescaling outside a problem context. If you want to map the range 1 - 100 to the range 1 - 10 linearly you should do: $$x \leftarrow \frac{x - 1}{99} \times 9 + 1$$ This maps 1 to 1 and 100 to 10 and it will make the durations have the same range as the other ...

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In addition to @NickCox 's answer, I think that standardizing, especially after transforming, makes the model harder to interpret. For example, suppose you are trying to predict the number of days spent on vacation per year. One of your IV is income in thousand of \$ per year. But, for several reasons, you take log of income. Then let's say you standardize ...

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Neither quite right nor quite wrong in my view. Standardization doesn't do much harm, but I don't think it is essential for any purpose. Historically, it was often important to have values of different predictors on similar levels for computational reasons, but decent software usually now takes care of that for you. It remains true that predictors measured ...

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