On 20/06/18 13:18, Moritz Lennert wrote:
Actually I noticed that your error message concerned the qbwwv voting
algorithm which wasn't in the command line you provided. I was just able
to reproduce your error using that same voting scheme. I'll have to dig
into this a bit deeper to see where the error comes from.
I think I found the issue: your training data is insufficient.
Here is the number of training segments for each class:
1 2 3 4 5 6 7 8 9 10 11 12 13 15 16 18 19 20
2 2 1 2 7 12 1 1 3 3 2 2 4 2 1 1 2 2
As you can see most classes have only 1-2 segments. The random forest
algorithm's crossvalidation works by randomly selecting subsets of
training data for each run. This means that using your training data set
it will very often have subsets with some classes not represented at
all, leading to warnings such as this:
50: model fit failed for Fold4.Rep02: mtry=60 Error in
randomForest.default(x, y, mtry = param$mtry, ...) :
Can't have empty classes in y.
This means that often the random forest algorithm cannot determine
accuracy measures through cross-validation. These accuracy measures are
used as weights in the weighted voting schemes.
You could still use v.class.mlR either by not using a tree based
approach such as random forest, or by not using a weighted vote, but
just a simple majority vote (the default). But I would not recommend it
in light of your limited training data. In general rule of thumb you
should have at least 30 training segments per class, but often you might
even need a bit more. Another solution would be to be less ambitious in
terms of number of classes. 20 is a lot. Often classifications target
5-10 classes.
Moritz
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