Wondering how the sketch would look like for the question below. **QUESTION FROM CHAPTER...

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Wondering how the sketch would look like for the question below.

**QUESTION FROM CHAPTER 8 -QUESTION 9 OF 'An Introduction to Statistical Learning with Applications in R**

2) Sketch the tree (part a), and the partition of the predictor space (part b), by hand.

PART A

```{r} set.seed(1) library(ISLR) train=sample(1:nrow(OJ),800,replace=F) OJtrain=OJ[train,] OJtest=OJ[-train,]

```

PART B

```{r} library(tree) tree.oj=tree(Purchase ~ ., data = OJtrain) summary(tree.oj) ```

PART B OUTCOME

Classification tree: tree(formula = Purchase ~ ., data = OJtrain) Variables actually used in tree construction: [1] "LoyalCH" "PriceDiff" "SpecialCH" "ListPriceDiff" "PctDiscMM" Number of terminal nodes: 9 Residual mean deviance: 0.7432 = 587.8 / 791 Misclassification error rate: 0.1588 = 127 / 800

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