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No it's not scary in the slightest. Missing or scrambled labels are routine in ML and the algorithms are able to handle it. This was a huge paper a couple year
by jasonwatkinspdx 7y ago
No it's not scary in the slightest. Missing or scrambled labels are routine in ML and the algorithms are able to handle it.
This was a huge paper a couple years ago that demonstrates deep nets will still find the structure in the data even with totally randomized labels: https://arxiv.org/abs/1611.03530 https://arxiv.org/abs/1611.03530
There are also various well known techniques to verify whether you've over or underfit.
- bo1024 7y agoStrongly disagree. ML is traditionally deployed in low-risk scenarios like image labeling or recommendations where 1% or 5% error rate is totally "routine" and acceptable. Doesn't mean this kind of ML pipeline can translate to cases where errors are literally fatal.
- sukilot 7y ago> deep nets will still find the structure in the data Yes, but in the training data, not in the non-training data! We want the cars to avoid real pedestrians, not only the ones labelled in the training set! From the paper: > When trained on a completely random labeling of the true data, neural networks achieve 0 training error. The test error, of course, is no better than random chance as there is no correlation between the training labels and the test labels.