ar, tree

1 files changed, 110 insertions, 0 deletions

diff --git a/R_Tree/main.r b/R_Tree/main.r
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+++ b/R_Tree/main.r
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+#!/usr/bin/env Rscript
+
+data <- read.csv("survey.csv")
+train <- head(data, 600)
+test <- tail(data, 150)
+
+if (!require(rpart)) install.packages("rpart", repos = "https://cran.r-project.org/", Ncpus = 16) # nolint
+library(rpart)
+library(rpart.plot)
+
+
+dt <- rpart(MYDEPV ~ Price + Income + Age,
+  data = train,
+  method = "class",
+  parms = list(split = "information"), # information gain splitting index
+  control = rpart.control(xval = 3) # three-fold cross-validation
+)
+
+printcp(dt)
+rpart.plot(dt, extra = 106)
+# summary(dt)
+
+
+tree_stats <- function(tree) {
+  frm <- tree$frame
+  internal_nodes <- sum(as.character(frm$var) != "<leaf>")
+
+  node_indexes <- as.integer(row.names(frm))
+  depth <- floor(log2(node_indexes)) + 1L
+
+  list(internal_nodes = as.integer(internal_nodes), height = as.integer(max(depth)))
+}
+
+stats <- tree_stats(dt)
+stats$internal_nodes
+stats$height
+
+
+pred <- predict(dt, train, type = "class")
+conf_matrix <- table(Predicted = pred, Actual = train$MYDEPV)
+conf_matrix
+
+misclass <- function(tt) {
+  # total_wrong / total_records
+  overall_misclass <- (sum(tt) - sum(diag(tt))) / sum(tt)
+  cat("Overall misclassification rate:", round(overall_misclass, 4), "\n")
+
+  classes <- rownames(tt)
+  misclass_per_class <- numeric(length(classes))
+  names(misclass_per_class) <- classes
+
+  for (cls in classes) {
+    correct <- tt[cls, cls]
+    total_in_class <- sum(tt[, cls])
+    misclass_per_class[cls] <- (total_in_class - correct) / total_in_class
+  }
+
+  cat("Misclassification rate per income class:\n")
+  print(round(misclass_per_class, 4))
+}
+
+misclass(conf_matrix)
+
+if (!require(ROCR)) install.packages("ROCR", repos = "https://cran.r-project.org/", Ncpus = 16) # nolint
+library(ROCR)
+
+rocr_pred <- prediction(predict(dt, type = "prob")[, 2], train$MYDEPV)
+roc <- performance(rocr_pred, "tpr", "fpr")
+auc <- performance(rocr_pred, "auc")
+
+plot(roc, col = "blue", main = "ROC")
+auc@y.values
+
+
+print("score with test data")
+pred <- predict(dt, test, type = "class")
+conf_matrix <- table(Predicted = pred, Actual = test$MYDEPV)
+conf_matrix
+misclass(conf_matrix)
+
+
+
+dt_gini <- rpart(MYDEPV ~ Price + Income + Age,
+  data = train,
+  method = "class",
+  parms = list(split = "gini"), # information gain splitting index
+  control = rpart.control(xval = 3) # three-fold cross-validation
+)
+
+printcp(dt_gini)
+rpart.plot(dt_gini, extra = 106)
+# summary(dt_gini)
+
+cp_table <- dt_gini$cptable
+optimal_cp <- cp_table[which.min(cp_table[, "xerror"]), "CP"]
+cat("Optimal CP value for pruning:", optimal_cp, "\n")
+dt_gini_pruned <- prune(dt_gini, cp = optimal_cp)
+
+printcp(dt_gini_pruned)
+rpart.plot(dt_gini_pruned, extra = 106)
+
+pred_gini <- predict(dt_gini_pruned, train, type = "class")
+conf_matrix_gini <- table(Predicted = pred_gini, Actual = train$MYDEPV)
+conf_matrix_gini
+
+misclass(conf_matrix_gini)
+
+stats <- tree_stats(dt_gini_pruned)
+stats$internal_nodes
+stats$height