r - 随机森林输出解释

Question

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

附言我希望将客户的个人资料作为输出，
例如来自纽约的人，从事技术行业等。

如何解释随机森林的结果？

Answer 1

“inTrees” R包可能有用。

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"

衡量规则。 len是条件中可变值对的数量，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"

建立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(请注意，未修剪或选择规则):

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"

人们还可以使用函数presentRules以可读形式显示这些频繁模式。

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}