我是否需要为 randomForest(R 包)标准化(或缩放)数据? [英] Do I need to normalize (or scale) data for randomForest (R package)?

问题描述

我正在做回归任务 - 我是否需要对 randomForest(R 包)的数据进行归一化(或缩放)?是否有必要扩展目标值?如果 - 我想使用 caret 包中的缩放功能，但我没有找到如何取回数据(去缩放，去规范化).你不知道其他一些有助于规范化/非规范化的函数(在任何包中)吗?谢谢，米兰

I am doing regression task - do I need to normalize (or scale) data for randomForest (R package)? And is it neccessary to scale also target values? And if - I want to use scale function from caret package, but I did not find how to get data back (descale, denormalize). Do not you know about some other function (in any package) which is helpfull with normalization/denormalization? Thanks, Milan

推荐答案

不，随机森林不需要缩放.

No, scaling is not necessary for random forests.

• RF 的本质是收敛和数值精度问题，这些问题有时会导致逻辑回归和线性回归以及神经网络中使用的算法不那么重要.因此，您无需像使用 NN 那样将变量转换为通用比例.

• 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.

您没有得到回归系数的任何类似物，它测量每个预测变量与响应之间的关系.因此，您也无需考虑如何解释这些受可变测量尺度影响的系数.

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.

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