I was reading about the solution to this OTTO Kaggle challenge and the first place solution seems to use several transforms for the input data X, for example Log(X+1), sqrt( X + 3/8), etc. Is there a general guideline on when to apply which kind transformations to various classifiers? I do understand the concepts of mean-var and min-max normalization. However, for the above transformations, my guess is that Log and Sqrt are used to compress the dynamic range of the data. And the x-axis shift is just to recenter the data. However, the author chooses to use different methods of normalization for the same input X when feeding into different classifiers. Any ideas?
Feature Transformation on Input data
machine learning;feature extraction;feature scaling