> Do I understand correctly - the "Locally weighted learning" method for
> regression evens out the difference in the number of instances for intervals
> of values by assigning certain weights to them?
At prediction time, the neighborhood gets determined on the fly and
weights for the instances of that subset calculated and set. The base
classifier then gets trained with that dataset. Whether the classifier
takes advantage of the weights depends on whether it implements the
WeightedInstancesHandler interface:
https://weka.sourceforge.io/doc.dev/weka/core/WeightedInstancesHandler.html> Where can these values of
> the assigned weights be seen after the LWL operation?
You can't, it happens behind the scenes inside LWL.
> And yet - for
> regression methods that are based on Gaussian distributions ( Gaussian
> processes for regression without hyperparameter-tuning) - the LWL method
> does not work, since it fits the uniform distribution. Or I'm wrong?
When GaussianProcesses became "weight-aware" I found that the
performance got worse... Not sure why.
Cheers, Peter
--
Peter Reutemann
Dept. of Computer Science
University of Waikato, NZ
+64 (7) 577-5304
http://www.cms.waikato.ac.nz/~fracpete/http://www.data-mining.co.nz/_______________________________________________
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