machine learning - normalize data and it's impact on error of prediction -


could tell me impact of normalization on absolute error , root mean square error? matter of truth, used normalize data , allso un-normalized data regression random forest algorithm, result (absolute error , rmse) differ significently!! example(with normalized data: absolute error=0.1014 , rmse=0.173 unnormalized data: absolute error=4.419 , rmes=7.57) i'm wondering these significant difference between normalized , unnormalized cases! explanation?

i found it's because of range of data in normalize , un-normalize form.


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