I have a database with 1200 observations and 14 variables and I'am trying to do a classification tree for my dependent nominal variable who hase 4 modality

    > table(testarbre2$Q99)

  Autres       Nahdha Ne pas voter Nidaa Tounes 
     248          351          303          298 

at firt i tried to do a multinom logistic regression but i got the mojority of my predictor variables non significant. it seems that Even with 1200 people I was trying to fit a model for which I don't have sufficient data. so i tried to do a classification tree using the package rpart from R but the problem is that the error is so high about 65% and more, and the missclassification is about 70% this is the code R that i used

   #preparation of the data

   #fitting the model
   Tree <- rpart(Q99~.,data=training_data)

    #Construction of the complete tree
  Tree <-rpart(Q99~.,data=training_data,control=rpart.control(minsplit=50,cp=0))

     #Prune the tree
    treeOptimal <- prune(Tree,cp=Tree$cptable[which.min(Tree$cptable[,4]),1])

   a=predict(ptitanicOptimal,testing_data2,type = "class")

I don't know if i missed a step or i used a wrong approach in the construction of my classification tree or the database is causing the problem

Please someone help me to understand what's wrong with my model

  • $\begingroup$ Unfortunately, asking for help w/ code, & code check, are off topic here. If you have a question about the statistical / machine learning aspects of this, please edit to clarify. Otherwise, this will probably be closed. $\endgroup$ Sep 3, 2016 at 12:32

1 Answer 1


Classification trees require sometimes ten times the sample size of logistic regression, and you will be quite disappointed in the stability of the tree. Bootstrap the process for a few resamples and you will see the tree topology change quite a bit. Simplicity in single trees is more of an illusion than a reality. Trees seem simple when you select one tree from many competitors that are very difficult to choose from. In addition you have chosen a discontinuous improper accuracy scoring rule which is optimized by bogus predictions, i.e., optimized by using the wrong model for the data.

Lack of significance is not a reason to change methods. Instead consider data reduction masked to $Y$, or use penalized maximum likelihood estimation to deal with your relatively small sample size.


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