Take the tour ×  For other algorithms (like support vector
machines), it is recommended that input attributes are scaled in
some way (for example put everything on a [0,1] scale). I have
googled extensively and can't find any information on whether
this needs to be done for boosting methods, and in particular
gradient tree boosting.  share|improve this question Since tree
boosting is basically using decision trees, you have to look up
for normalisation for decision trees. –  BlueTrin Nov 2 '12 at
18:21 add comment  2 Answers  For class prediction (vs. class
discovery), I always transform (remove scale effects) for input
features. Metrics for features with larger scale (range) can
swamp metrics of features with smaller scale. Depending on your
method (there are numerous boosting approaches), you might also
consider decorrelating features. A golden rule regarding
correlated features, at least with artificial neural networks,
is that during connection weight learning time will be spent on
figuring out the correl