```
library("randomForest")
# Fit a Random Forest on the Boston housing data set
data("Boston", package = "MASS")
rf <- randomForest(medv ~ ., data = Boston, ntree = 50)
# Create a model object
mod <- Predictor$new(rf, data = Boston[-which(names(Boston) == "medv")])
# Fit a decision tree as a surrogate for the whole random forest
dt <- TreeSurrogate$new(mod)
# Plot the resulting leaf nodes
plot(dt)
# Use the tree to predict new data
predict(dt, Boston[1:10, ])
# Extract the results
dat <- dt$results
head(dat)
# It also works for classification
rf <- randomForest(Species ~ ., data = iris, ntree = 50)
X <- iris[-which(names(iris) == "Species")]
mod <- Predictor$new(rf, data = X, type = "prob")
# Fit a decision tree as a surrogate for the whole random forest
dt <- TreeSurrogate$new(mod, maxdepth = 2)
# Plot the resulting leaf nodes
plot(dt)
# If you want to visualize the tree directly:
plot(dt$tree)
# Use the tree to predict new data
set.seed(42)
iris.sample <- X[sample(1:nrow(X), 10), ]
predict(dt, iris.sample)
predict(dt, iris.sample, type = "class")
# Extract the dataset
dat <- dt$results
head(dat)
```

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