Reverse scaling data/ Unscaling data in R

In machine learning, data should usually be scaled before feeding to the training model since variables might be in different kinds of ranges. If not scaled, it shall make the prediction far from accuracy when calculating the distances among  data.

However, we still need a prediction result in the original range, which is the reverse of scaling.

In r, we could do this:

attributes(d$s.x)

d$s.x * attr(d$s.x, 'scaled:scale') + attr(d$s.x, 'scaled:center')

for example:

> x <- 1:10
> s.x <- scale(x)
> s.x
            [,1]
 [1,] -1.4863011
 [2,] -1.1560120
 [3,] -0.8257228
 [4,] -0.4954337
 [5,] -0.1651446
 [6,]  0.1651446
 [7,]  0.4954337
 [8,]  0.8257228
 [9,]  1.1560120
[10,]  1.4863011
attr(,"scaled:center")
[1] 5.5
attr(,"scaled:scale")
[1] 3.02765
> s.x * attr(s.x, 'scaled:scale') + attr(s.x, 'scaled:center')
      [,1]
 [1,]    1
 [2,]    2
 [3,]    3
 [4,]    4
 [5,]    5
 [6,]    6
 [7,]    7
 [8,]    8
 [9,]    9
[10,]   10
attr(,"scaled:center")
[1] 5.5
attr(,"scaled:scale")
[1] 3.02765

ref:
http://stackoverflow.com/questions/10287545/backtransform-scale-for-plotting

If it helps, you could also try the R library:
http://www.inside-r.org/packages/cran/DMwR/docs/unscale