如何使用R在随机森林中生成决策树图和变量重要性图？

Question

I am new to Data Science and I am working on a Machine Learning analysis using Random Forest algorithm to perform a classification. My target variable in my data set is called Attrition (Yes/No).

I am a bit confused as to how to generate these 2 plots in Random Fores`:

(1) Feature Importance Plot

(2) Decision Tree Plot

I understand that Random Forest is a ensemble of several Decision Tree models from the data set.

Assuming my Training data set is called TrainDf and my Testing data set is called TestDf , how can I create these 2 plots in R?

UPDATE: From these 2 posts, it seems that they cannot be done, or am I missing something here? Why is Random Forest with a single tree much better than a Decision Tree classifier?

How would you interpret an ensemble tree model?

Answer 1

To plot the variable importance, you can use the below code.

mtcars.rf <- randomForest(am ~ ., data=mtcars, ntree=1000, keep.forest=FALSE,
                      importance=TRUE)
varImpPlot(mtcars.rf)

Answer 2

Feature importance plot with ggplot2 ,

library(randomForest)
library(ggplot2)
mtcars.rf <- randomForest(vs ~ ., data=mtcars)
imp <- cbind.data.frame(Feature=rownames(mtcars.rf$importance),mtcars.rf$importance)
g <- ggplot(imp, aes(x=reorder(Feature, -IncNodePurity), y=IncNodePurity))
g + geom_bar(stat = 'identity') + xlab('Feature')

A Decision Tree plot with igraph (a tree from the random forest)

tree <- randomForest::getTree(mtcars.rf, k=1, labelVar=TRUE) # get the 1st decision tree with k=1
tree$`split var` <- as.character(tree$`split var`)
tree$`split point` <- as.character(tree$`split point`)
tree[is.na(tree$`split var`),]$`split var` <- ''
tree[tree$`split point` == '0',]$`split point` <- ''

library(igraph)
gdf <- data.frame(from = rep(rownames(tree), 2),
                          to = c(tree$`left daughter`, tree$`right daughter`))
g <- graph_from_data_frame(gdf, directed=TRUE)
V(g)$label <- paste(tree$`split var`, '\r\n(', tree$`split point`, ',', round(tree$prediction,2), ')')
g <- delete_vertices(g, '0')
print(g, e=TRUE, v=TRUE)
plot(g, layout = layout.reingold.tilford(g, root=1), vertex.size=5, vertex.color='cyan')

As can be seen from the following plot, the the label for each node in the decision tree represents the variable name chosen for split at that node, (the split value, the proportion of class with label 1) at that node.

Likewise the 100th tree can be obtained with k=100 with the randomForest::getTree() function which looks like the following