Way to get list of the actual "nearest neighbors"?
keith
New Altair Community Member
When applying a NearestNeighbor model, is there a way to return the list nearest neighbor points for each predicted value?
In other words, if I run an existing KNN model to predict new values on 100 new examples, where K=5, I want to return the five nearest neighbor point matches for each example. Something like
Example# Neighbor ID
1 1 AAA
1 2 BBB
1 3 CCC
1 4 DDD
1 5 EEE
2 1 FFF
2 2 CCC
2 3 GGG
2 4 AAA
2 5 HHH
3 1 JJJ
etc.
Even if it doesn't rank the neighbors, just getting a list of neighbors for each example row would be great. Any suggestions are appreciated.
Keith
In other words, if I run an existing KNN model to predict new values on 100 new examples, where K=5, I want to return the five nearest neighbor point matches for each example. Something like
Example# Neighbor ID
1 1 AAA
1 2 BBB
1 3 CCC
1 4 DDD
1 5 EEE
2 1 FFF
2 2 CCC
2 3 GGG
2 4 AAA
2 5 HHH
3 1 JJJ
etc.
Even if it doesn't rank the neighbors, just getting a list of neighbors for each example row would be great. Any suggestions are appreciated.
Keith
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0
Answers
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Hi Keith,
I do not know if this is possible with RapidMiner without checking, but I assume that it is at least not a one-operator-process ..
However, I would start by using the [tt]ExampleSet2SimilarityExampleSet[/tt] operator in combination with some filtering techniques ... did you try that already?
Regards,
Tobias0 -
Thanks Tobias. I wasn't aware of the similiarity operators. That's almost exactly what I'm looking for. Is there a version that can compute similarity distances using attribute weights?
Keith
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Hi Keith,
since there is neither parameter to specify if attribute weights are considered nor an input indicating that attribute weights might be processed, I assume that attribute weights may not be considered. But in conjunction with numerical attribute weights, you may use the [tt]AttributeWeightsApplier[/tt] before using one of the similarity calculation operator.
Cheers,
Tobias0 -
Cool! I will try that. Thanks again.
Keith
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