Decision Tree CrossValidation Error of .07 How to determine if acceptable??

taking this example of coding from documentation:

    leafs=logspace(1,2,10);
    N=numel(leafs);
    err=zeros(N,1);
    for n=1:N
        t=fitctree(x,y,'CrossVal','On','MinLeaf',leafs(n));
        err(n)=kfoldLoss(t);
    end
    plot(leafs,err)

the data I used in this resulted in a chart showing errors ranging from about .06 to .09.

Does anyone know how to interpret this error result?

Answer 1

[Bruno Pop-Stefanov]

Cross validation is a technique to assess how well a predictor can generalize to new data. The lower the cross validation error, the better. Having a CV error of .07 tells you that 93% of the test data set was correctly classified. Whether this is a good score depends on your application and your expectations. The point of this chart is to compare the performance of the classification tree for various leaf sizes. This chart will let you know what you should choose for the leaf size in order to get the best classification accuracy possible.

[Justin]

Just saw this - Thanks Bruno