如何繪製R樹隨機樹的ROC曲線？

Question

我知道randomForest應該是一個黑盒子，而且大多數人都對整個分類器的ROC曲線感興趣，但是我正在研究一個需要檢查RF單個樹的問題。我對R並不是很有經驗，因此繪製RF生成的單個樹的ROC曲線的簡單方法是什麼？

Answer 1

我不認爲你可以從randomForest包生成的隨機森林中的單個樹生成ROC曲線。您可以通過預測訪問每棵樹的輸出，例如通過訓練集。

# 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") 

您可以訪問每個樹的預測與

rf_pred$individual 

然而，一棵樹的預測是唯一最有可能的標籤。對於ROC曲線，您需要類別概率，以便更改決策閾值可更改預測類別以改變真實和錯誤的肯定率。

據我所知，至少在包randomForest中，沒有辦法使葉子輸出概率而不是標籤。如果你用getTree（）檢查一棵樹，你會看到預測是二進制的;使用getTree（rf_model，k = 1，labelVar = TRUE），您將看到明文標籤。

但是，您可以執行的操作是通過predict.all = TRUE檢索單個預測，然後在整個森林的子集上手動計算類標籤。然後，您可以輸入函數來計算ROCR曲線，如ROCR軟件包中的曲線。

編輯：好的，從您在評論中提供的鏈接中獲得瞭如何獲得ROC曲線的想法。首先，我們需要提取一棵特定的樹，然後將每個數據點輸入到樹中，以便計算每個節點上成功類的發生次數以及每個節點中的總數據點。該比率給出了成功等級的節點概率。接下來，我們做類似的事情，即將每個數據點輸入到樹中，但現在記錄概率。這樣我們可以比較類的probs和真實的標籤。 這裏是代碼：

# 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"))