Tal,
For the Gini criterion, the "improve" value can be calculated as a
weighted sum of the improvement in impurity. Continuing with your
original code:
# for "gini"
impurity_root<- gini(prop.table(table(y)))
impurity_l<- gini(prop.table(table(obs_0)))
impurity_R<-gini(prop.table(table(obs_1)))
# (13 and 7 are sample sizes in respective nodes)
13*(impurity_root - impurity_l) + 7*(impurity_root - impurity_R)
[1] 5.384615
This does not appear to extend immediately to the information criterion,
however. I'm not sure about the 6.84.
Ed
On 6/14/11 5:00 AM, r-help-requ...@r-project.org wrote:
------------------------------
Message: 4
Date: Mon, 13 Jun 2011 15:47:26 +0300
From: Tal Galili<tal.gal...@gmail.com>
To:r-help@r-project.org
Subject: [R] In rpart, how is "improve" calculated? (in the "class"
case)
Message-ID:<BANLkTimp1aFQoYrKina7H0Rnk=0zkr_...@mail.gmail.com>
Content-Type: text/plain
Hi all,
I apologies in advance if I am missing something very simple here, but since
I failed at resolving this myself, I'm sending this question to the list.
I would appreciate any help in understanding how the rpart function is
(exactly) computing the "improve" (which is given in fit$split), and how it
differs when using the split='information' vs split='gini' parameters.
According to the help in rpart.object:
"improve, which is the improvement in deviance given by this split"
From what I understand, that would mean that the "improve" value should not
be different when using different "split" switches. Since it is different,
then I suspect that it is reflecting the impurity measure somehow, but I
can't seem to understand how exactly.
Bellow is some simple R code showing the result for a simple classification
tree, with what the function outputs, and what I would have expected to see
if "improve" were to simply reflect the change in impurity.
set.seed(1324)
y<- sample(c(0,1), 20, T)
x<- y
x[1:5]<- 0
require(rpart)
fit<- rpart(y~x, method = "class", parms=list(split='information'))
fit$split[,3] # why is improve here 6.84 ?
fit<- rpart(y~x, method = "class", parms=list(split='gini'))
fit$split[,3] # why is improve here 5.38 ?
# Here is what I thought it should have been:
# for "information"
entropy<- function(p) {
if(any(p==1)) return(0) # works for the case when y has only 0 and 1
categories...
-sum(p*log(p,2))
}
gini<- function(p) {sum(p*(1-p))}
obs_1<- y[x>.5]
obs_0<- y[x<.5]
n_l<- sum(x>.5)
n_R<- sum(x<.5)
n<- length(x)
# for entropy (information)
impurity_root<- entropy(prop.table(table(y)))
impurity_l<- entropy(prop.table(table(obs_0)))
impurity_R<-entropy(prop.table(table(obs_1)))
# shouldn't this have been "improve" ??
impurity_root - ((n_l/n)*impurity_l + (n_R/n)*impurity_R) # 0.7272
# for "gini"
impurity_root<- gini(prop.table(table(y)))
impurity_l<- gini(prop.table(table(obs_0)))
impurity_R<-gini(prop.table(table(obs_1)))
impurity_root - ((n_l/n)*impurity_l + (n_R/n)*impurity_R) # 0.3757
Thanks upfront,
Tal
----------------Contact
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Contact me:tal.gal...@gmail.com | 972-52-7275845
Read me:www.talgalili.com (Hebrew) |www.biostatistics.co.il (Hebrew) |
www.r-statistics.com (English)
----------------------------------------------------------------------------------------------
--
*** Note new email address ***
Ed Merkle, PhD
Assistant Professor
Department of Psychological Sciences (starting August 2011)
University of Missouri
Columbia, MO, USA 65211
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