如何绘制R树随机树的ROC曲线？

Question

我知道randomForest应该是一个黑盒子，而且大多数人都对整个分类器的ROC曲线感兴趣，但是我正在研究一个需要检查RF单个树的问题。我对R并不是很有经验，因此绘制RF生成的单个树的ROC曲线的简单方法是什么？

Answer 1

我不认为你可以从randomForest包生成的随机森林中的单个树生成ROC曲线。您可以通过预测访问每棵树的输出，例如通过训练集。

# caret for an example data set 
library(caret) 
library(randomForest) 

data(GermanCredit) 

# use only 50 rows for demonstration 
nrows = 50 

# extract the first 9 columns and 50 rows as training data (column 10 is "Class", the target) 
x = GermanCredit[1:nrows, 1:9] 
y = GermanCredit$Class[1:nrows] 

# build the model 
rf_model = randomForest(x = x, y = y, ntree = 11) 

# Compute the prediction over the training data. Note predict.all = TRUE 
rf_pred = predict(rf_model, newdata = x, predict.all = TRUE, type = "prob") 

您可以访问每个树的预测与

rf_pred$individual 

然而，一棵树的预测是唯一最有可能的标签。对于ROC曲线，您需要类别概率，以便更改决策阈值可更改预测类别以改变真实和错误的肯定率。

据我所知，至少在包randomForest中，没有办法使叶子输出概率而不是标签。如果你用getTree（）检查一棵树，你会看到预测是二进制的;使用getTree（rf_model，k = 1，labelVar = TRUE），您将看到明文标签。

但是，您可以执行的操作是通过predict.all = TRUE检索单个预测，然后在整个森林的子集上手动计算类标签。然后，您可以输入函数来计算ROCR曲线，如ROCR软件包中的曲线。

编辑：好的，从您在评论中提供的链接中获得了如何获得ROC曲线的想法。首先，我们需要提取一棵特定的树，然后将每个数据点输入到树中，以便计算每个节点上成功类的发生次数以及每个节点中的总数据点。该比率给出了成功等级的节点概率。接下来，我们做类似的事情，即将每个数据点输入到树中，但现在记录概率。这样我们可以比较类的probs和真实的标签。 这里是代码：

# libraries we need 
library(randomForest) 
library(ROCR) 

# Set fixed seed for reproducibility 
set.seed(54321) 

# Define function to read out output node of a tree for a given data point 
travelTree = function(tree, data_row) { 
    node = 1 
    while (tree[node, "status"] != -1) { 
     split_value = data_row[, tree[node, "split var"]] 
     if (tree[node, "split point"] > split_value) { 
      node = tree[node, "right daughter"] 
     } else { 
      node = tree[node, "left daughter"] 
     } 
    } 
    return(node) 
} 

# define number of data rows 
nrows = 100 
ntree = 11 

# load example data 
data(GermanCredit) 

# Easier access of variables 
x = GermanCredit[1:nrows, 1:9] 
y = GermanCredit$Class[1:nrows] 

# Build RF model 
rf_model = randomForest(x = x, y = y, ntree = ntree, nodesize = 10) 

# Extract single tree and add variables we need to compute class probs 
single_tree = getTree(rf_model, k = 2, labelVar = TRUE) 
single_tree$"split var" = as.character(single_tree$"split var") 
single_tree$sum_good = 0 
single_tree$sum = 0 
single_tree$pred_prob = 0 


for (zeile in 1:nrow(x)) { 
    out_node = travelTree(single_tree, x[zeile, ]) 
    single_tree$sum_good[out_node] = single_tree$sum_good[out_node] + (y[zeile] == "Good") 
    single_tree$sum[out_node] = single_tree$sum[out_node] + 1 
} 

# Compute class probabilities from count of "Good" data points in each node. 
# Make sure we do not divide by zero 
idcs = single_tree$sum != 0 
single_tree$pred_prob[idcs] = single_tree$sum_good[idcs]/ single_tree$sum[idcs] 

# Compute prediction by inserting again data set into tree, but read out 
# previously computed probs 

single_tree_pred = rep(0, nrow(x)) 

for (zeile in 1:nrow(x)) { 
    out_node = travelTree(single_tree, x[zeile, ]) 
    single_tree_pred[zeile] = single_tree$pred_prob[out_node] 
} 

# Et voila: The ROC curve for single tree! 
plot(performance(prediction(single_tree_pred, y), "tpr", "fpr"))