Growing some Trees

Consider here the dataset used in a previous post, about visualising a classification (with more than 2 features),

> MYOCARDE=read.table(
+ "",
+ header=TRUE,sep=";")

The default classification tree is

> arbre = rpart(factor(PRONO)~.,data=MYOCARDE)
> rpart.plot(arbre,type=4,extra=6)

We can change the options here, such as the minimum number of observations, per node

> arbre = rpart(factor(PRONO)~.,data=MYOCARDE,
+       control=rpart.control(minsplit=10))
> rpart.plot(arbre,type=4,extra=6)


> arbre = rpart(factor(PRONO)~.,data=MYOCARDE,
+        control=rpart.control(minsplit=5))
> rpart.plot(arbre,type=4,extra=6)

Continue reading Growing some Trees

How social media usage does and does not predict protests

This post, published today in Monkey Cage, is based on some research, with Marco T. Bastos and Dan Mercea (recently published in the Journal of Communication, see also

A storm of protests in 2011 disputed the legitimacy of the institutional status quo across authoritarian and democratic countries alike. From the Arab Spring to the Indignados, from the London riots to the Occupy movement, a heated debate has ensued about whether people’s coming onto the streets may be aided in any significant manner by the use of social media. The debate has lingered for a decade now and is revisited with every new uprising that rises to international prominence.

Continue reading How social media usage does and does not predict protests

Growing one Tree

Consider the following toy dataset, with some spam/ham information, and two words, “viagra” and “lottery”.

> load(spam.RData)
> head(db)
      Y viagra lottery
27 spam      0       1
37  ham      0       1
57 spam      0       0
89  ham      0       0
20 spam      1       0
86  ham      0       0

For the first node, compute Gini index for the two variables,

> gini=function(variable){
+ T=table(db$Y,db[,variable])
+ nx=apply(T,2,sum)
+ ProbCond=T/matrix(rep(nx,each=2),2,2)
+ ProbCond
+ Gini=-ProbCond*(1-ProbCond)
+ sum(matrix(rep(nx,each=2),2,2)/sum(nx)*Gini)}
> gini("viagra")
[1] -0.44
> gini("lottery")
[1] -0.487

Here Gini index is maximal for “viagra”, so that will be the first node.

Continue reading Growing one Tree