如何从插入符号：：训练对象绘制最终的c50决策树模型(库C50)

Question

我使用来自插入符号库的train函数训练了决策树模型：

gr = expand.grid(trials = c(1, 10, 20), model = c("tree", "rules"), winnow = c(TRUE, FALSE))
dt = train(y ~ ., data = train, method = "C5.0", trControl = trainControl(method = 'cv', number = 10), tuneGrid = gr)

现在，我想为最终模型绘制决策树。但是这个命令不起作用：

plot(dt$finalModel)

Error in data.frame(eval(parse(text = paste(obj$call)[xspot])), eval(parse(text = paste(obj$call)[yspot])),  : 
  arguments imply differing number of rows: 4160, 208, 0

有人已经在这里问过了：主题

建议的解决方案是使用bestTune从列车对象中手动定义相应的c5.0模型。然后绘制c5.0模型，通常如下：

c5model = C5.0(x = x, y = y, trials = dt$bestTune$trials, rules = dt$bestTune$model == "rules", control = C5.0Control(winnow = dt$bestTune$winnow))
plot(c5model)

我试过这样做。是的，它使得绘制c5.0模型、成为可能，但是预测了训练对象和手动重新创建c5.0模型的概率不匹配。

因此，我的问题是:是否可以从caret::train对象中提取最终的c5.0模型并绘制此决策树

Answer 1

预测的概率应相同，见下文：

library(MASS)
library(caret)
library(C50)
library(partykit)

traindata = Pima.tr
testdata = Pima.te

gr = expand.grid(trials = c(1, 2), 
model = c("tree"), winnow = c(TRUE, FALSE))

dt = train(x = traindata[,-ncol(testdata)], y = traindata[,ncol(testdata)], 
method = "C5.0",trControl = trainControl(method = 'cv', number=3),tuneGrid=gr)

c5model = C5.0.default(x = traindata[,-ncol(testdata)], y = traindata[,ncol(testdata)], 
trials = dt$bestTune$trials, rules = dt$bestTune$model == "rules", 
control = C5.0Control(winnow = dt$bestTune$winnow))

all.equal(predict(c5model,testdata[,-ncol(testdata)],type="prob"),
predict(dt$finalModel,testdata[,-ncol(testdata)],type="prob"))
[1] TRUE

所以我建议你再检查一下预测是否相同。

从插入符号中看到的绘制最终模型的错误来自于存储在$call下的内容(这很奇怪)，我们可以用一个调用来替换它，该调用可以用于绘图：

plot(c5model)

​

​

finalMod = dt$finalModel
finalMod$call = c5model$call
plot(finalMod)

​

​

或者你可以用你的训练结果重写它，但是你可以看到它的表达变得有点复杂(或者至少我对它不太擅长)：

newcall = substitute(C5.0.default(x = X, y = Y, trials = ntrials, rules = RULES, control = C5.0Control(winnow = WINNOW)),
list(
X = quote(traindata[, -ncol(traindata)]),
Y = quote(traindata[, ncol(traindata)]),
RULES = dt$bestTune$model == "rules",
ntrials = dt$bestTune$trials,
WINNOW = dt$bestTune$winnow)
)

finalMod = dt$finalModel
finalMod$call = newcall