feature weightage vs domain inputs.
hi all,
When I am trying to use 'explain predictions' - it comes out with various weightage of features which varies with selection of algorithm as well.
For eg: going for kNN - will choose feature A, feature B, feature C, feature D as top 3.
1. However my domain knowledge says feature D is the most important one. in that case
selection of kNN ( for which feature D is not important ) will do the job even if it gives good accuracy during training and testing?
2. or in the above scenario - should I go for model say: SVM - which naturally consider feature D as most important attribute ? , but the performance of SVM is less comparin with kNN for the given data set during training
and testing.
can I have some clarity on how to approach.. particularly when there is conflict in order of preference by weightage sugessted by explain prediction operator while comparing with domain inputs. thanks.
regards
thiru
When I am trying to use 'explain predictions' - it comes out with various weightage of features which varies with selection of algorithm as well.
For eg: going for kNN - will choose feature A, feature B, feature C, feature D as top 3.
1. However my domain knowledge says feature D is the most important one. in that case
selection of kNN ( for which feature D is not important ) will do the job even if it gives good accuracy during training and testing?
2. or in the above scenario - should I go for model say: SVM - which naturally consider feature D as most important attribute ? , but the performance of SVM is less comparin with kNN for the given data set during training
and testing.
can I have some clarity on how to approach.. particularly when there is conflict in order of preference by weightage sugessted by explain prediction operator while comparing with domain inputs. thanks.
regards
thiru