Classification trees are nice. They provide an interesting alternative to a logistic regression. I started to include them in my courses maybe seven or eight years ago. The question is nice (how to get an optimal partition), the algorithmic procedure is nice (the trick of splitting according to one variable, and only one, at each node, and then to move forward, never backward), and the visual output is just perfect (with that tree structure). But the prediction can be rather poor. The performance of that algorithm can hardly compete with a (well specified) logistic regression.

Then I discovered forests (see Leo Breiman’s page for a detailed presentation). Being a huge fan of boostrap procedures I loved the idea. In regression models, I usually mention bootstrap to avoid asymptotic approximations: we bootstrap the rows (the observations). In the case of random forest, I have to admit that the idea of selecting randomly a set of possible variables at each node is very clever. The performance is much better, but interpretation is usually more difficult. And, something that I love when there is a lot of covariance: the variable importance plot. Which is something that we can hardly get with econometric models (please let me know if I’m wrong).

In order to illustrate, let us generate a large dataset. Not necessarily huge, but large, so that we really have to select variables. Since it is more interesting if we have possibly correlated variables, we need a covariance matrix. There is a nice package in R to randomly generate covariance matrices.

```
> set.seed(1)
> n=500
> library(clusterGeneration)
> library(mnormt)
> S=genPositiveDefMat("eigen",dim=15)
> S=genPositiveDefMat("unifcorrmat",dim=15)
> X=rmnorm(n,varcov=S$Sigma)
> library(corrplot)
> corrplot(cor(X), order = "hclust")
```

See Gosh & Hendersen (2003) for more details on the methodology.

Now that we have a covariance matrix, let us generate a dataset,

```
> Score=-1+X[,1]/sd(X[,1])+X[,2]/sd(X[,2])+
+ 0.5*X[,3]/sd(X[,3])-0.25*X[,4]/sd(X[,4])
> P=exp(Score)/(1+exp(Score))
> Y=rbinom(n,size=1,prob=P)
> df=data.frame(Y,X)
> allX=paste("X",1:ncol(X),sep="")
> names(df)=c("Y",allX)
```

The variable importance plot is obtained by growing some trees,

```
> require(randomForest)
> fit=randomForest(factor(Y)~., data=df)
```

Then we can use simple functions

```
> (VI_F=importance(fit))
MeanDecreaseGini
X1 31.14309
X2 31.78810
X3 20.95285
X4 13.52398
X5 13.54137
X6 10.53621
X7 10.96553
X8 15.79248
X9 14.19013
X10 10.02330
X11 11.46241
X12 11.36008
X13 10.82368
X14 10.17462
X15 10.45530
```

which can also be obtained using

```
> library(caret)
> varImp(fit)
Overall
X1 31.14309
X2 31.78810
X3 20.95285
X4 13.52398
X5 13.54137
X6 10.53621
X7 10.96553
X8 15.79248
X9 14.19013
X10 10.02330
X11 11.46241
X12 11.36008
X13 10.82368
X14 10.17462
X15 10.45530
```

But the popular plot that we see in all reports is usually

`> varImpPlot(fit,type=2)`

which represents the *mean decrease in node impurity* (and not the *mean decrease in accuracy*). One can also visualise Partial Response Plots, as suggested in Friedman (2001), in the context of boosting,

```
> importanceOrder=order(-fit$importance)
> names=rownames(fit$importance)[importanceOrder][1:15]
> par(mfrow=c(5, 3), xpd=NA)
> for (name in names)
+ partialPlot(fit, df, eval(name), main=name, xlab=name,ylim=c(-.2,.9))
```

Those variable importance functions can be obtained on simple trees, not necessarily forests. As discussed in a previous post, given an impurity function such as Gini index we split at some node ** t ** if the change in the index

is significant, where ** tL** is the node on the left, and

**the node on the right.**

*tR*It is possible to evalute the importance of some variable * Xk* when predicting

**by adding up the weighted impurity decreases for all nodes**

*Y**where*

**t***is used (averaged over all trees in the forest, but actually, we can use it on a single tree),*

**Xk**

where the second sum is only on nodes * t* based on variable

**. If**

*Xk**is Gini index, then*

**i(.)***is called*

**I(.)****Mean Decrease Gini**function.

Consider a single tree, just to illustrate, as suggested in some old post on http://stats.stackexchange.com/

```
> library(rpart)
> fit=rpart(factor(Y)~., df)
> plot(fit)
> text(fit)
```

The idea is look at each node which variable was used to split, and to store it, and then to compute some average (see http://stats.stackexchange.com/)

```
> tmp=rownames(fit$splits)
> allVars=colnames(attributes(fit$terms)$factors)
> rownames(fit$splits)=1:nrow(fit$splits)
> splits=data.frame(fit$splits)
> splits$var=tmp
> splits$type=""
> frame=as.data.frame(fit$frame)
> index=0
> for(i in 1:nrow(frame)){
+ if(frame$var[i] != "<leaf>"){
+ index=index + 1
+ splits$type[index]="primary"
+ if(frame$ncompete[i] > 0){
+ for(j in 1:frame$ncompete[i]){
+ index=index + 1
+ splits$type[index]="competing"}}
+ if(frame$nsurrogate[i] > 0){
+ for(j in 1:frame$nsurrogate[i]){
+ index=index + 1
+ splits$type[index]="surrogate"}}}}
> splits$var=factor(as.character(splits$var))
> splits=subset(splits, type != "surrogate")
> out=aggregate(splits$improve,
+ list(Variable = splits$var),
+ sum, na.rm = TRUE)
> allVars=colnames(attributes(fit$terms)$factors)
> if(!all(allVars %in% out$Variable)){
+ missingVars=allVars[!(allVars %in% out$Variable)]
+ zeros=data.frame(x = rep(0, length(missingVars)), Variable = missingVars)
+ out=rbind(out, zeros)}
> out2=data.frame(Overall = out$x)
> rownames(out2)=out$Variable
> out2
Overall
X1 51.024692
X10 4.328443
X11 19.087255
X12 10.399549
X13 15.248933
X15 9.989834
X2 68.758329
X3 41.986055
X4 15.211913
X5 18.247668
X7 18.857998
X8 43.318540
X9 30.299429
X6 0.000000
X14 0.000000
```

This is the variable influence table we got on our original tree

```
> VI_T=out2
> barplot(unlist(VI_T/sum(VI_T)),names.arg=1:15)
```

If we compare we the one on the forest, we get something rather similar

`> barplot(t(VI_F/sum(VI_F)))`

This graph is a great tool for variable selection, when we have a lot of variables. And we can get it on a single tree, if it is deep enough.

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