Unpredictable results.
rhythmize opened this issue · 5 comments
Over the same dataset I trained on different machines, the misclassified samples are different.
Although few of the all data samples are misclassified over the given dataset, the results varies from machine to machine. Test samples properly identified on one machine are mislabeled on the other.
Do you have any idea about this?
The algorithms have randomness inherently.
So, there's no way the results can be certain ?
right.
@Code-Player by mislabeled you mean the results are incorrect & the matching is not showing correct results ?
Yeah. Definitely. Over the same dataset (say A-Z) trained over two different machines. One machine misidentifies some users (let say D, M, P) and perfectly identifies the rest, the other machine misidentifies some other users, (like F, O, X) and identifies the rest properly.
This was the issue I asked about.