随机森林是特点鲜明的模型，不是万能的模型
Random forest is a specific algorithm, not omnipotent for all datasets
李欣海1, 2**
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DOI：10.7679/j.issn.2095-1353.2019.022
作者单位：1. 中国科学院动物研究所，北京 100101；2. 中国科学院大学，北京 100049
中文关键词：随机森林；偏效应；交互作用；多元共线性；R语言
英文关键词：random forest; partial effect; interaction; multicollinearity; R
中文摘要：
随机森林（Random forest）模型在2001年发表后得到广泛的关注。由于随机森林可以进行回归和判别等多种统计分析，而且不受正态性、方差齐性和自变量独立性等参数检验的前提条件的制约，其应用日益普遍，有被看作万能模型的趋势。实际上，随机森林是一种特点鲜明的模型，应用局部优化拟合观察值，在分析有偏效应关系的数据时，其结果往往不准确。本文以蝉科（Cicadidea）物种的分布数据为例，比较了随机森林在回归分析时与多元线性回归、广义可加模型和人工神经网络模型的差别，在判别分析时与线性判别分析的差别，强调了随机森林预测时的碎片化特点。结果显示随机森林在处理有多元共线性和交互作用的数据时，以及在判别分析时，其准确率最高。鉴于随机森林的局限性，建议做数据分析时选择多种模型进行比较。文中的R语言代码可为研究者提供参考。
英文摘要：
Random forest has gained extensive
attention since its publication in 2001. Random forest can handle both
regression and classification with minimum assumptions (no need for normality,
homogeneity of variance, and independence between explanatory variables), so
that its applications has dramatically increased. Someone even use it as an
omnipotent tool for all analysis. In fact, random forest is a specific
algorithm with clear characteristics. It is an ensemble method by constructing
a number of decision trees, which intends to use local optimization to fit
data. When the data have strong partial effect, random forest usually does not
fit well. I compared the performance of random forest with multiple regression
models, generalized additive models, and artificial neural network using the
occurrence data of Cicadidea species. The results showed, although the
prediction of random forest looked fragmented, it outperformed the other three
models. Random forest also performed better than linear discriminant analysis
for classifications. Random forest has its strength and weakness. I suggestion
to use multiple models for data analysis rather than one “powerful” model.