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I am trying to classify set of text files whether it belong to category A or category B using KNN algorithm

Here is the error that i get

> knn.pred <- knn(tdm.stack.nl, tdm.stack.nl_test, tdm.cand)  

Error in knn(tdm.stack.nl, tdm.stack.nl_test, tdm.cand) : dims of 'test' and 'train' differ

 > dim(tdm.stack.nl)  
    [1]  184 1599  
    > dim(tdm.stack.nl_test)  
    [1]   1 992  

As obvious the number of words used by training dataset is different from train. How can I avoid this?

I want to mention that this error i get when i divide the training and test dataset manually to (70:30) ratio. However i dont get this error if i divide the training and test dataset using samlpling method.

> # create hold-out   
> set.seed(500)  
> train.idx <- sample(nrow(tdm.stack), ceiling(nrow(tdm.stack) * 0.7))   
> test.idx <- (1:nrow(tdm.stack))[-train.idx]   
> 
> # create model - knn clustering
> tdm.cand <- tdm.stack[, "targetcandidate"]
> tdm.stack.nl <- tdm.stack[, !colnames(tdm.stack) %in% "targetcandidate"]
> 
> # set up model
> knn.pred <- knn(tdm.stack.nl[train.idx,], tdm.stack.nl[test.idx,],    tdm.cand[train.idx])  
> dim(tdm.stack.nl[train.idx,])  
[1]  129 1599  
> dim(tdm.stack.nl[test.idx,])  
[1]   55 1599   

However this method will not work for me as I want to classify a document in a real time using the model that was built before.

Please help. Kindly let me know if you need any other info. Arun

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  • $\begingroup$ I am facing the same issue which the same data set as above library(tm) library(plyr) #for keeping the text as-is options(stringasFactors=F) #dicrectory location team<-c("AA","BB") storedin<-"XXXX" #pre-process; one corpus for each candidate cleanCorpus <- function(corpus){ mycorpus <- tm_map(corpus, removePunctuation) mycorpus <- tm_map(mycorpus,stripWhitespace) mycorpus <- tm_map(mycorpus, PlainTextDocument) mycorpus <- tm_map(mycorpus,removeWords, stopwords("english")) return(mycorpus) } #tdm mytdm<-function(teamember,storedpath){ emaillocation<-sprintf("%s/%s",storedpath,teamember) newcorp $\endgroup$ – rohit kumar Sep 12 '16 at 14:51
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You are avoiding the error with sampling purely by chance (the words are common). Simplest way would be to merge training and test, construct a tdm on a joined set, separate into training and test again and then purge some columns (the constant ones in the train set, as they correspond to words occurring in test only => useless for training).

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  • $\begingroup$ Thanks for the answer. I will try this. In the meanwhile, I have a doubt. Lets say that i want to build the above classification model now, and reuse that later to classify the documents later, how can i do that? Given that we have to perform the TDM as a single step for both training and test dataset, will saving a model and reusing it will work? $\endgroup$ – Arun Aug 31 '15 at 18:18
  • $\begingroup$ @Arun: In Python, you can fit the transformation and store it as a pickle for later use. Closest counterpart in R I can think of is dumping the fitted model (TDM) in an .RData file and then filtering a new test set (matching the column names) prior to use. Crude, but it might get the job done. $\endgroup$ – kpb Sep 2 '15 at 8:40
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add 'Drop' in cl. It will work.

Example: cl = train_labels[,1, drop = TRUE] knn(train_points, test_points, cl, k = 5)

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