【发布时间】：2019-02-15 05:45:28
【问题描述】：
我承认，除了编写它们的人之外，向任何人提出这个问题都有些困难，但我在 R 中随机森林的三个不同版本中获得了持续不同的结果。
有问题的三个方法是 randomForest 包、插入符号中的“rf”方法和 ranger 包。代码如下。
有问题的数据是一个例子；我在类似数据的其他规范中看到了类似的情况。
LHS 变量：政党标识（Dem、Rep、Indep.）。右手边的预测变量是人口统计数据。为了弄清楚 bizarre results in the randomForest package 到底发生了什么，我尝试在其他两种方法中实现相同的模型。我发现它们不会重现该特定异常；这特别奇怪，因为据我所知，插入符号中的 rf 方法只是对 randomForest 包的间接使用。
我在每个实现中运行的三个规范是 (1) 三类分类，(2) 删除独立类别，以及 (3) 与 2 相同但将单个观察结果加扰到“独立”以保留三个类别该模型应该会产生与 2 相似的结果。据我所知，在任何情况下都不应该有任何过度或不足抽样来解释结果。
我还注意到以下趋势：
- randomForest 包是唯一一个完全混乱的包，只有两个类别。
- ranger 包始终将更多的观察（正确和错误）识别为独立的。
- ranger 包在整体预测准确性方面总是稍差一些。
- caret 包的整体准确度与 randomForest 相似（略高），但始终在更常见的类中更好，在不太常见的类中更差。这很奇怪，因为据我所知，在这两种情况下我都没有实施任何过采样或欠采样，而且我认为 caret 依赖于 randomForest 包。
下面我包含了代码和混淆矩阵，显示了所讨论的差异。每次重新运行代码都会在混淆矩阵中产生相似的趋势；这不是“任何单独的运行都可能产生奇怪的结果”的问题。
有没有人知道为什么这些软件包会始终产生略微不同的结果（并且在 randomForest 中的链接问题的情况下，非常不同）的结果通常，甚至更好，为什么它们会以这种特定方式有所不同？例如，我应该注意这些包的包中是否有某种样本加权/分层？
代码：
num_trees=1001
var_split=3
load("three_cat.Rda")
rf_three_cat <-randomForest(party_id_3_cat~{RHS Vars},
data=three_cat,
ntree=num_trees,
mtry=var_split,
type="classification",
importance=TRUE,confusion=TRUE)
two_cat<-subset(three_cat,party_id_3_cat!="2. Independents")
two_cat$party_id_3_cat<-droplevels(two_cat$party_id_3_cat)
rf_two_cat <-randomForest(party_id_3_cat~{RHS Vars},
data=two_cat,
ntree=num_trees,
mtry=var_split,
type="classification",
importance=TRUE,confusion=TRUE)
scramble_independent<-subset(three_cat,party_id_3_cat!="2. Independents")
scramble_independent[1,19]<-"2. Independents"
scramble_independent<- data.frame(lapply(scramble_independent, as.factor), stringsAsFactors=TRUE)
rf_scramble<-randomForest(party_id_3_cat~{RHS Vars},
data=scramble_independent,
ntree=num_trees,
mtry=var_split,
type="classification",
importance=TRUE,confusion=TRUE)
ranger_2<-ranger(formula=party_id_3_cat~{RHS Vars},
data=two_cat,
num.trees=num_trees,mtry=var_split)
ranger_3<-ranger(formula=party_id_3_cat~{RHS Vars},
data=three_cat,
num.trees=num_trees,mtry=var_split)
ranger_scram<-ranger(formula=party_id_3_cat~{RHS Vars},
data=scramble_independent,
num.trees=num_trees,mtry=var_split)
rfControl <- trainControl(method = "none", number = 1, repeats = 1)
rfGrid <- expand.grid(mtry = c(3))
rf_caret_3 <- train(party_id_3_cat~{RHS Vars},
data=three_cat,
method="rf", ntree=num_trees,
type="classification",
importance=TRUE,confusion=TRUE,
trControl = rfControl, tuneGrid = rfGrid)
rf_caret_2 <- train(party_id_3_cat~{RHS Vars},
data = two_cat,
method = "rf",ntree=num_trees,
type="classification",
importance=TRUE,confusion=TRUE,
trControl = rfControl, tuneGrid = rfGrid)
rf_caret_scramble <- train(party_id_3_cat~{RHS Vars},
data = scramble_independent,
method = "rf",ntree=num_trees,
type="classification",
importance=TRUE,confusion=TRUE,
trControl = rfControl, tuneGrid = rfGrid)
rf_three_cat$confusion
ranger_3$confusion.matrix
rf_caret_3$finalModel["confusion"]
rf_two_cat$confusion
ranger_2$confusion.matrix
rf_caret_2$finalModel["confusion"]
rf_scramble$confusion
ranger_scram$confusion.matrix
rf_caret_scramble$finalModel["confusion"]
结果（格式稍作修改以供比较）：
> rf_three_cat$confusion
1. Democrats (including leaners) 2. Independents 3. Republicans (including leaners) class.error
1. Democrats (including leaners) 1121 3 697 0.3844042
2. Independents 263 7 261 0.9868173
3. Republicans (including leaners) 509 9 1096 0.3209418
> ranger_3$confusion.matrix
1. Democrats (including leaners) 2. Independents 3. Republicans (including leaners) class.error
1. Democrats (including leaners) 1128 46 647 0.3805601
2. Independents 263 23 245 0.9566855
3. Republicans (including leaners) 572 31 1011 0.3736059
> rf_caret_3$finalModel["confusion"]
1. Democrats (including leaners) 2. Independents 3. Republicans (including leaners) class.error
1. Democrats (including leaners) 1268 0 553 0.3036793
2. Independents 304 0 227 1.0000000
3. Republicans (including leaners) 606 0 1008 0.3754647
> rf_two_cat$confusion
1. Democrats (including leaners) 3. Republicans (including leaners) class.error
1. Democrats (including leaners) 1775 46 0.0252608
3. Republicans (including leaners) 1581 33 0.9795539
> ranger_2$confusion.matrix
1. Democrats (including leaners) 3. Republicans (including leaners) class.error
1. Democrats (including leaners) 1154 667 0.3662823
3. Republicans (including leaners) 590 1024 0.3655514
> rf_caret_2$finalModel["confusion"]
1. Democrats (including leaners) 3. Republicans (including leaners) class.error
1. Democrats (including leaners) 1315 506 0.2778693
3. Republicans (including leaners) 666 948 0.4126394
> rf_scramble$confusion
1. Democrats (including leaners) 2. Independents 3. Republicans (including leaners) class.error
1. Democrats (including leaners) 1104 0 717 0.3937397
2. Independents 0 0 1 1.0000000
3. Republicans (including leaners) 501 0 1112 0.3106014
> ranger_scram$confusion.matrix
1. Democrats (including leaners) 2. Independents 3. Republicans (including leaners)
1. Democrats (including leaners) 1159 0 662 0.3635365
2. Independents 0 0 1 1.0000000
3. Republicans (including leaners) 577 0 1036 0.3577185
> rf_caret_scramble$finalModel["confusion"]
1. Democrats (including leaners) 2. Independents 3. Republicans (including leaners) class.error
1. Democrats (including leaners) 1315 0 506 0.2778693
2. Independents 0 0 1 1.0000000
3. Republicans (including leaners) 666 0 947 0.4128952
【问题讨论】：
-
仅供参考，插入符号方法
rf在后台使用randomForest包：topepo.github.io/caret/train-models-by-tag.html#random-forest -
是的，我知道。正如我在第 4 点差异点中提到的，这就是为什么我发现它们产生不同的结果如此令人惊讶的部分原因。
标签： r machine-learning random-forest r-caret