resampling an unbalanced dataset

Hi, I have a dataset which has 2 classes(churn='False.' and churn='True.'). It is unbalanced because 700 of the 5000 sample is churn='False.' Is there a way to balance that distribution? Thank you in advance.

Answer 1

[Image Analyst]

Throw out all but 700 items where churn = true??? Then you'd have 700 false ones and 700 true ones. If not, then tell us in more detail what "balance" means to you.

[Ege]

What I mean is that for example approximately 2500 of the samples belongs to the churn='False' class whereas the remaining 2500 belongs to churn='True' class which makes it unbalanced.

[Ege]

I have 700 churn=False which means remaining 4300 belongs to the other class (churn=True). so do you mean I should do it manually like delete the 3600 of the 4300 and create 700 & 700 balanced data?

[Image Analyst]

Uh, sure, if that's what you want. If it's in a table, you can automate it somewhat, like

% Find out which rows are true.
trueRows = find(t.churn);
% Take only the first 700:
trueRows  = trueRows(1:max([length(trueRows), 700]));
% Find out which rows are false - we want to keep all those.
falseRows = find(t.churn == false);
% Combine the false and true rows into one list of indexes.
rowsToExtract = sort([falseRows, trueRows]);
% Now extract only the first 700 true, but all the false.
t = t(rowsToExtract );

or something like that. You might have to debug it some.