随机森林产出解释

Question

我为我的数据运行了一个随机的森林,并以矩阵的形式得到了输出.它适用于分类的规则是什么？

PS我希望客户的个人资料作为输出,例如来自纽约的人,在科技行业工作,等等.

如何解释随机森林的结果？

Answer 1

查看每个树应用的规则

假设你使用这个randomForest包,这就是你如何访问森林里的树木.

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

这显示了500的树#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
...

您开始阅读描述根分割的第一行.根分裂基于变量3,即如果Petal.Length <= 2.50继续到左子节点(第2行)并且如果Petal.Length > 2.50继续到右子节点(第3行).如果一条线的状态-1如第2行那样,则意味着我们已经到达一个叶子并且将进行预测,在这种情况下是类1,即 setosa.

这些都是在手册中写的,所以请看一下?randomForest并?getTree了解更多细节.

考虑整个森林的变化重要性

看看?importance和?varImpPlot.这样,您可以在整个森林中汇总每个变量的单个分数.

> importance(rf)
             MeanDecreaseGini
Sepal.Length         10.03537
Sepal.Width           2.31812
Petal.Length         43.82057
Petal.Width          43.10046

Answer 2

" inTrees "R包可能很有用.

这是一个例子.

从随机林中提取原始规则:

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"

衡量规则.len是条件中变量值对的数量,freq是满足条件的数据的百分比,pred是规则的结果,即condition=> pred,err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(注意规则未被修剪或选择):

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}

Answer 3

除了上面的好答案之外，我还发现了另一个有趣的工具，旨在探索随机森林的一般输出：函数explain_forest包randomForestExplainer。请参阅此处了解更多详情。

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)

这将生成一个.html文件，命名Your_forest_explained.html，在你的my/destination/path，你可以在Web浏览器中轻松打开。

在此报告中，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，请参见下面的已生长森林的树木之间的最小深度分布图

或多向重要性图之一

您可以参考本报告的解读。