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Decision trees are applied to situation where data is divided into groups rather than investigating a numerical response and its relationship to a set of descriptor variables. There are various implementations of classification trees in **R** and the some commonly used functions are **rpart** and **tree**.

Fast Tube by Casper

To illustrate the use of the **tree** function we will use a set of data from the UCI Machine Learning Repository where the objective of the study using this data was to *predict the cellular localization sites of proteins*.

The data provided on the website is shown here:

> ecoli.df = read.csv("ecoli.txt") > head(ecoli.df) Sequence mcv gvh lip chg aac alm1 alm2 class 1 AAT_ECOLI 0.49 0.29 0.48 0.5 0.56 0.24 0.35 cp 2 ACEA_ECOLI 0.07 0.40 0.48 0.5 0.54 0.35 0.44 cp 3 ACEK_ECOLI 0.56 0.40 0.48 0.5 0.49 0.37 0.46 cp 4 ACKA_ECOLI 0.59 0.49 0.48 0.5 0.52 0.45 0.36 cp 5 ADI_ECOLI 0.23 0.32 0.48 0.5 0.55 0.25 0.35 cp 6 ALKH_ECOLI 0.67 0.39 0.48 0.5 0.36 0.38 0.46 cp

We can use the **xtabs** function to summarise the number of cases in each class.

> xtabs( ~ class, data = ecoli.df) class cp im imL imS imU om omL pp 143 77 2 2 35 20 5 52

As noted in the comments the package that I used was the **tree** package:

> require(tree)

The complete classification tree using all variables is fitted to the data initially and then we will try to *prune* the tree to make it smaller.

> ecoli.tree1 = tree(class ~ mcv + gvh + lip + chg + aac + alm1 + alm2, data = ecoli.df) > summary(ecoli.tree1) Classification tree: tree(formula = class ~ mcv + gvh + lip + chg + aac + alm1 + alm2, data = ecoli.df) Variables actually used in tree construction: [1] "alm1" "mcv" "gvh" "aac" "alm2" Number of terminal nodes: 10 Residual mean deviance: 0.7547 = 246 / 326 Misclassification error rate: 0.122 = 41 / 336

The **tree** function is used in a similar way to other modelling functions in **R**. The misclassification rate is shown as part of the summary of the tree. This tree can be plotted and annotated with these commands:

> plot(ecoli.tree1) > text(ecoli.tree1, all = T)

To prune the tree we use cross-validation to identify the point to *prune*.

> cv.tree(ecoli.tree1) $size [1] 10 9 8 7 6 5 4 3 2 1 $dev [1] 463.6820 457.4463 447.9824 441.8617 455.8318 478.9234 533.5856 586.2820 713.2992 1040.3878 $k [1] -Inf 12.16500 15.60004 19.21572 34.29868 41.10627 50.57044 64.05494 180.78800 355.67747 $method [1] "deviance" attr(,"class") [1] "prune" "tree.sequence"

This suggests a tree size of 6 and we can re-fit the tree:

> ecoli.tree2 = prune.misclass(ecoli.tree1, best = 6) > summary(ecoli.tree2) Classification tree: snip.tree(tree = ecoli.tree1, nodes = c(4, 20, 7)) Variables actually used in tree construction: [1] "alm1" "mcv" "aac" "gvh" Number of terminal nodes: 6 Residual mean deviance: 0.9918 = 327.3 / 330 Misclassification error rate: 0.1548 = 52 / 336

The misclassification rate has increased but not substantially with the *pruning* of the tree.

Other useful resources are provided on the Supplementary Material page.

Data used in this post: Ecoli Data Set.

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