```
library("rpart")
cross_validate <- function(df, tree, n_iter, split_ratio, method = 'class')
{
# training data frame df
df <- as.data.frame(df)
# mean_subset is a vector of accuracy values generated from the specified features in the tree object
mean_subset <- c()
# mean_all is a vector of accuracy values generated from all the available features in the data frame
mean_all <- c()
# control parameters for the decision tree
contro = tree$control
# the following snippet will create relations to generate decision trees
# relation_all will create a decision tree with all the features
# relation_subset will create a decision tree with only user-specified features in tree
dep <- all.vars(terms(tree))[1]
indep <- list()
relation_all = as.formula(paste(dep, '.', sep = "~"))
i <- 1
while (i < length(all.vars(terms(tree)))) {
indep[[i]] <- all.vars(terms(tree))[i + 1]
i <- i + 1
}
b <- paste(indep, collapse = "+")
relation_subset <- as.formula(paste(dep, b, sep = "~"))
# creating train and test samples with the given split ratio
# performing cross-validation n_iter times
for (i in 1:n_iter) {
sample <-
sample.int(n = nrow(df),
size = floor(split_ratio * nrow(df)),
replace = F)
train <- df[sample,]
testing <- df[-sample,]
type = typeof(unlist(testing[dep]))
# decision tree for regression if the method specified is "anova"
if (method == 'anova') {
first.tree <-
rpart(
relation_subset,
data = train,
control = contro,
method = 'anova'
)
second.tree <- rpart(relation_all, data = train, method = 'anova')
pred1.tree <- predict(first.tree, newdata = testing)
pred2.tree <- predict(second.tree, newdata = testing)
mean1 <- mean((as.numeric(pred1.tree) - testing[, dep]) ^ 2)
mean2 <- mean((as.numeric(pred2.tree) - testing[, dep]) ^ 2)
mean_subset <- c(mean_subset, mean1)
mean_all <- c(mean_all, mean2)
}
# decision tree for classification
# if the method specified is not "anova", then this block is executed
# if the method is not specified by the user, the default option is to perform classification
else{
first.tree <-
rpart(
relation_subset,
data = train,
control = contro,
method = 'class'
)
second.tree <- rpart(relation_all, data = train, method = 'class')
pred1.tree <- predict(first.tree, newdata = testing, type = 'class')
pred2.tree <-
predict(second.tree, newdata = testing, type = 'class')
mean1 <-
mean(as.character(pred1.tree) == as.character(testing[, dep]))
mean2 <-
mean(as.character(pred2.tree) == as.character(testing[, dep]))
mean_subset <- c(mean_subset, mean1)
mean_all <- c(mean_all, mean2)
}
}
# average_accuracy_subset is the average accuracy of n_iter iterations of cross-validation with user-specified features
# average_acuracy_all is the average accuracy of n_iter iterations of cross-validation with all the available features
# variance_accuracy_subset is the variance of accuracy of n_iter iterations of cross-validation with user-specified features
# variance_accuracy_all is the variance of accuracy of n_iter iterations of cross-validation with all the available features
cross_validation_stats <-
list(
"average_accuracy_subset" = mean(mean_subset, na.rm = T),
"average_accuracy_all" = mean(mean_all, na.rm = T),
"variance_accuracy_subset" = var(mean_subset, na.rm = T),
"variance_accuracy_all" = var(mean_all, na.rm = T)
)
# creating a data frame of accuracy_subset and accuracy_all
# accuracy_subset contains n_iter accuracy values on cross-validation with user-specified features
# accuracy_all contains n_iter accuracy values on cross-validation with all the available features
cross_validation_df <-
data.frame(accuracy_subset = mean_subset, accuracy_all = mean_all)
return(list(cross_validation_df, cross_validation_stats))
}
```

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