根据随机森林分类绘制ROC曲线 [英] Plotting a ROC curve from a random forest classification

问题描述

我正在尝试绘制随机森林分类的​​ROC曲线。进行绘图是可行的，但是我认为我正在打印错误的数据，因为生成的绘图仅具有一个点（精度）。

I'm trying to plot ROC curve of a random forest classification. Plotting works, but I think I'm plotting the wrong data since the resulting plot only has one point (the accuracy).

这是我使用的代码：

set.seed(55)
data.controls <- cforest_unbiased(ntree=100, mtry=3)
data.rf <- cforest(type ~ ., data = dataset ,controls=data.controls) 
pred <- predict(data.rf, type="response")
preds <- prediction(as.numeric(pred), dataset$type)
    perf <- performance(preds,"tpr","fpr")
    performance(preds,"auc")@y.values
    confusionMatrix(pred, dataset$type)

plot(perf,col='red',lwd=3)
abline(a=0,b=1,lwd=2,lty=2,col="gray")

推荐答案

要绘制运行曲线的接收器，您需要移交分类器的连续输出，例如后验概率。也就是说，您需要进行预测（data.rf，newdata，类型= prob ）。

To plot a receiver operating curve you need to hand over continuous output of the classifier, e.g. posterior probabilities. That is, you need to predict (data.rf, newdata, type = "prob").

<带有 type = response 的code>预测已经为您提供了强化因素作为输出。因此，您的工作点已经隐式地固定了。

predicting with type = "response" already gives you the "hardened" factor as output. Thus, your working point is implicitly fixed already. With respect to that, your plot is correct.

边注：在袋中对随机森林的预测将非常乐观！

side note: in bag prediction of random forests will be highly overoptimistic!

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