One final tree using more options

In this exercise, you will use some final arguments that were discussed in the video. Some specifications in the rpart.control()-function will be changed, and some weights will be included using the weights argument in rpart(). The vector case_weights has been constructed for you and is loaded in your workspace. This vector contains weights of 1 for the non-defaults in the training set, and weights of 3 for defaults in the training sets. By specifying higher weights for default, the model will assign higher importance to classifying defaults correctly.

Bu egzersiz

Credit Risk Modeling in R

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Set a seed of 345.
Add to the provided code by passing case_weights to the weights argument of `rpart().
Change the minimum number of splits that are allowed in a node to 5, and the minimum number of observations allowed in leaf nodes to 2 by using the arguments minsplit and minbucket in rpart.control respectively.
Use function plotcp() to investigate where the cross-validated error rate can be minimized.
Use which.min() to identify the row with the minimum "xerror" in tree_weights$cp. Assign this to index.
Use the provided code to select the cp for which the crossvalidated error is minimized
Prune the tree using the complexity parameter where the cross-validated error rate is minimized. Store the pruned tree in ptree_weights.
Plot the pruned tree using function prp(). Include a second argument extra and set it equal to 1.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# set a seed and run the code to obtain a tree using weights, minsplit and minbucket
set.seed(345)
tree_weights <- rpart(loan_status ~ ., method = "class",
                      data = training_set,
                      control = rpart.control(minsplit = ___, minbucket = ___, cp = 0.001))

# Plot the cross-validated error rate for a changing cp


# Create an index for of the row with the minimum xerror
index <- which.min(___$___[ , "xerror"])

# Create tree_min
tree_min <- tree_weights$cp[index, "CP"]

# Prune the tree using tree_min


# Plot the pruned tree using the rpart.plot()-package

Kodu Düzenle ve Çalıştır