Yeah, XGB still outperforms knn and svm here. There's a bunch of other non-coordinate covariates that contribute and XGB just kicks butt in this case. Fair enough, thanks for the response!

What's the best way to go about a multiclassification problem in which 3 of the features are x, y, z coordinates, with each row only having one location per outcome?

I'd like to take advantage within the model of the idea that there is spatial correlation in the outcomes (e.g. one record close to another in x, y, z will likely have a similar outcome). The spatial components make me want to use a CNN, but each input being just a 1x3 vector rather than something bigger makes me think that's not possible?

(fwiw, xgboost has the best predictive accuracy. Tried a gaussian process too but XGB still beat it. Was thinking there might be a NN approach but google has not been fruitful)