我需要为随机森林(R 包)规范化(或缩放)数据吗？

I do not see any suggestions in either the help page or the Vignette that suggests scaling is necessary for a regression variable in randomForest. This example at Stats Exchange does not use scaling either.

Copy of my comment: The scale function does not belong to pkg:caret. It is part of the "base" R package. There is an unscale function in packages grt and DMwR that will reverse the transformation, or you could simply multiply by the scale attribute and then add the center attribute values.

Your conception of why "normalization" needs to be done may require critical examination. The test of non-normality is only needed after the regressions are done and may not be needed at all if there are no assumptions of normality in the goodness of fit methodology. So: Why are you asking? Searching in SO and Stats.Exchange might prove useful: citation #1 ; citation #2 ; citation #3

The boxcox function is a commonly used tranformation when one does not have prior knowledge of twhat a distribution "should" be and when you really need to do a tranformation. There are many pitfalls in applying transformations, so the fact that you need to ask the question raises concerns that you may be in need of further consultations or self-study.

No, scaling is not necessary for random forests.

• The nature of RF is such that convergence and numerical precision issues, which can sometimes trip up the algorithms used in logistic and linear regression, as well as neural networks, aren't so important. Because of this, you don't need to transform variables to a common scale like you might with a NN.

• You're don't get any analogue of a regression coefficient, which measures the relationship between each predictor variable and the response. Because of this, you also don't need to consider how to interpret such coefficients which is something that is affected by variable measurement scales.

If you are going to add interactions to dataset - that is, new variable being some function of other variables (usually simple multiplication), and you dont feel what that new variable stands for (cant interprete it), then you should calculate this variable using scaled variables.

Guess, what will happen in the following example? Imagine, you have 20 predictive features, 18 of them are in [0;10] range and the other 2 in [0;1,000,000] range (taken from a real-life example). Question1: what feature importances will Random Forest assign. Question2: what will happen to the feature importance after scaling the 2 large-range features?

Scaling is important. It is that Random Forest is less sensitive to the scaling then other algorithms and can work with "roughly"-scaled features.

Scaling is done to Normalize data so that priority is not given to a particular feature. Role of Scaling is mostly important in algorithms that are distance based and require Euclidean Distance.

Random Forest is a tree-based model and hence does not require feature scaling.

This algorithm requires partitioning, even if you apply Normalization then also> the result would be the same.

Random Forest uses information gain / gini coefficient inherently which will not be affected by scaling unlike many other machine learning models which will (such as k-means clustering, PCA etc). However, it might 'arguably' fasten the convergence as hinted in other answers