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我为我的数据运行了一个随机森林,并以矩阵的形式获得了输出。它适用于分类的规则是什么?

PS我想要一个客户的个人资料作为输出,例如来自纽约的人,在技术行业工作等。

如何解释随机森林的结果?

4

3 回答 3

40

inTrees ” R 包可能很有用。

这是一个例子。

从随机森林中提取原始规则:

library(inTrees)
library(randomForest) 
data(iris)
X <- iris[, 1:(ncol(iris) - 1)]  # X: predictors
target <- iris[,"Species"]  # target: class
rf <- randomForest(X, as.factor(target))
treeList <- RF2List(rf)  # transform rf object to an inTrees' format
exec <- extractRules(treeList, X)  # R-executable conditions
exec[1:2,]
#       condition                 
# [1,] "X[,1]<=5.45 & X[,4]<=0.8"
# [2,] "X[,1]<=5.45 & X[,4]>0.8"

测量规则。len是条件中的变量值对的数量,freq是满足条件的数据的百分比,pred是规则的结果,即condition=> prederr是规则的错误率。

ruleMetric <- getRuleMetric(exec,X,target)  # get rule metrics
ruleMetric[1:2,]
#      len  freq    err     condition                  pred        
# [1,] "2" "0.3"   "0"     "X[,1]<=5.45 & X[,4]<=0.8" "setosa"    
# [2,] "2" "0.047" "0.143" "X[,1]<=5.45 & X[,4]>0.8"  "versicolor"

修剪每条规则:

ruleMetric <- pruneRule(ruleMetric, X, target)
ruleMetric[1:2,]
#      len  freq    err     condition                 pred        
# [1,] "1" "0.333" "0"     "X[,4]<=0.8"              "setosa"    
# [2,] "2" "0.047" "0.143" "X[,1]<=5.45 & X[,4]>0.8" "versicolor"

选择一个紧凑的规则集:

(ruleMetric <- selectRuleRRF(ruleMetric, X, target))
#          len freq    err     condition                                             pred         impRRF              
# [1,] "1" "0.333" "0"     "X[,4]<=0.8"                                          "setosa"     "1"                 
# [2,] "3" "0.313" "0"     "X[,3]<=4.95 & X[,3]>2.6 & X[,4]<=1.65"               "versicolor" "0.806787615686919" 
# [3,] "4" "0.333" "0.04"  "X[,1]>4.95 & X[,3]<=5.35 & X[,4]>0.8 & X[,4]<=1.75"  "versicolor" "0.0746284932951366"
# [4,] "2" "0.287" "0.023" "X[,1]<=5.9 & X[,2]>3.05"                             "setosa"     "0.0355855756152103"
# [5,] "1" "0.307" "0.022" "X[,4]>1.75"                                          "virginica"  "0.0329176860493297"
# [6,] "4" "0.027" "0"     "X[,1]>5.45 & X[,3]<=5.45 & X[,4]<=1.75 & X[,4]>1.55" "versicolor" "0.0234818254947883"
# [7,] "3" "0.007" "0"     "X[,1]<=6.05 & X[,3]>5.05 & X[,4]<=1.7"               "versicolor" "0.0132907201116241"

构建一个有序规则列表作为分类器:

(learner <- buildLearner(ruleMetric, X, target))
#      len freq                 err                  condition                                             pred        
# [1,] "1" "0.333333333333333"  "0"                  "X[,4]<=0.8"                                          "setosa"    
# [2,] "3" "0.313333333333333"  "0"                  "X[,3]<=4.95 & X[,3]>2.6 & X[,4]<=1.65"               "versicolor"
# [3,] "4" "0.0133333333333333" "0"                  "X[,1]>5.45 & X[,3]<=5.45 & X[,4]<=1.75 & X[,4]>1.55" "versicolor"
# [4,] "1" "0.34"               "0.0196078431372549" "X[,1]==X[,1]"                                        "virginica" 

使规则更具可读性:

readableRules <- presentRules(ruleMetric, colnames(X))
readableRules[1:2, ]
#      len  freq    err     condition                                                                       pred        
# [1,] "1" "0.333" "0"     "Petal.Width<=0.8"                                                              "setosa"    
# [2,] "3" "0.313" "0"     "Petal.Length<=4.95 & Petal.Length>2.6 & Petal.Width<=1.65"                     "versicolor"

提取频繁的变量交互(注意没有修剪或选择规则):

rf <- randomForest(X, as.factor(target))
treeList <- RF2List(rf)  # transform rf object to an inTrees' format
exec <- extractRules(treeList, X)  # R-executable conditions
ruleMetric <- getRuleMetric(exec, X, target)  # get rule metrics
freqPattern <- getFreqPattern(ruleMetric)
# interactions of at least two predictor variables
freqPattern[which(as.numeric(freqPattern[, "len"]) >= 2), ][1:4, ]
#      len sup     conf    condition                  pred        
# [1,] "2" "0.045" "0.587" "X[,3]>2.45 & X[,4]<=1.75" "versicolor"
# [2,] "2" "0.041" "0.63"  "X[,3]>4.75 & X[,4]>0.8"   "virginica" 
# [3,] "2" "0.039" "0.604" "X[,4]<=1.75 & X[,4]>0.8"  "versicolor"
# [4,] "2" "0.033" "0.675" "X[,4]<=1.65 & X[,4]>0.8"  "versicolor"

还可以使用函数 presentRules 以可读的形式呈现这些频繁模式。

此外,可以在 LaTex 中格式化规则或频繁模式。

library(xtable)
print(xtable(freqPatternSelect), include.rownames=FALSE)
# \begin{table}[ht]
# \centering
# \begin{tabular}{lllll}
#   \hline
#   len & sup & conf & condition & pred \\ 
#   \hline
#   2 & 0.045 & 0.587 & X[,3]$>$2.45 \& X[,4]$<$=1.75 & versicolor \\ 
#   2 & 0.041 & 0.63 & X[,3]$>$4.75 \& X[,4]$>$0.8 & virginica \\ 
#   2 & 0.039 & 0.604 & X[,4]$<$=1.75 \& X[,4]$>$0.8 & versicolor \\ 
#   2 & 0.033 & 0.675 & X[,4]$<$=1.65 \& X[,4]$>$0.8 & versicolor \\ 
#   \hline
# \end{tabular}
# \end{table}
于 2014-07-21T20:48:35.567 回答
39

查看每棵树应用的规则

假设您使用该randomForest软件包,这就是您访问森林中适合的树木的方式。

library(randomForest)
data(iris)
rf <- randomForest(Species ~ ., iris)
getTree(rf, 1)

这显示了 500 棵树 #1 的输出:

   left daughter right daughter split var split point status prediction
1              2              3         3        2.50      1          0
2              0              0         0        0.00     -1          1
3              4              5         4        1.65      1          0
4              6              7         4        1.35      1          0
5              8              9         3        4.85      1          0
6              0              0         0        0.00     -1          2
...

您从描述根拆分的第一行开始阅读。根分裂基于变量 3,如果Petal.Length <= 2.50继续到左子节点(第 2 行),如果Petal.Length > 2.50继续到右子节点(第 3 行)。如果一行的状态是-1,因为它在第 2 行,这意味着我们已经到达一个叶子并且将进行预测,在这种情况下是 class 1 setosa

它实际上都写在手册中,因此请查看?randomForest?getTree了解更多详细信息。

查看整个森林的可变重要性

看看?importance?varImpPlot。这为您在整个森林中聚合的每个变量提供了一个分数。

> importance(rf)
             MeanDecreaseGini
Sepal.Length         10.03537
Sepal.Width           2.31812
Petal.Length         43.82057
Petal.Width          43.10046
于 2013-02-21T08:52:27.140 回答
6

除了上面的好答案之外,我还发现了另一种有趣的工具,旨在探索随机森林的一般输出:function explain_forestthe package randomForestExplainer。有关详细信息,请参见此处

示例代码:

library(randomForest)
data(Boston, package = "MASS")
Boston$chas <- as.logical(Boston$chas)
set.seed(123)
rf <- randomForest(medv ~ ., data = Boston, localImp = TRUE)

请注意:localImp必须设置为TRUE,否则explain_forest将退出并出现错误

library(randomForestExplainer)
setwd(my/destination/path)
explain_forest(rf, interactions = TRUE, data = Boston)

这将在您的文件中生成一个名为.html的文件,您可以在 Web 浏览器中轻松打开该文件。Your_forest_explained.htmlmy/destination/path

在本报告中,您将找到有关树木和森林结构的有用信息以及有关变量的一些有用统计信息。

例如,请参见下面的生长森林树木之间的最小深度分布图

在此处输入图像描述

或多向重要性图之一

在此处输入图像描述

报告的解读可以参考这里。

于 2018-10-26T13:02:19.007 回答