3 ms·

I am not afraid to outsource domain-agnostic projects to smart junior practitioners, though. Your conclusion is a cultural barrier where metrics is the real kin

by DrNuke 8y ago

I am not afraid to outsource domain-agnostic projects to smart junior practitioners, though. Your conclusion is a cultural barrier where metrics is the real king. Domain expertise is absolutely needed in a few specific cases only, the rest being gatekeeping.

nightski 8y ago
I was maybe a bit too snarky, apologize if so. But the crux of it is that his model never achieved > 50% accuracy. It was never necessary because he already got the job. But the thing is, that is the hard part. Closing those gaps in not just accuracy, but also generalization is the data science portion of the task (and requires much more knowledge than what is demonstrated by this blog post - although they could of left out a lot of detail). They make it seem like if he just had a little bit more time this would of been straightforward. But I am not sure about that. I am all for giving junior practitioners a chance. But this is like hiring an english major for aerospace engineering because they built a model airplane in my opinion. But maybe I vastly underestimate the amount of extremely low hanging fruit out there for ML projects.
- DrNuke 8y ago
  You’re welcome and not snarky at all. I am not sure you work with commercially-driven ML projects? It really depends on the metrics only. I can’t trust a ML black box for cancer screening (both false negatives and false positives have a big cost) or complex industrial failures (stopping a plant starts a lot of expensive compensating manouvres) but nobody sane in his mind has a real problem with 88, 90 or 92% accuracy for online retail recommenders (all the field is a kind of magic beyond a certain baseline and money never earned is not money lost from a bookkeeping perspective) or language translation (which is still human-proofed wherever it gets legal value of any kind). Hope I made my point clearer. Cheers.
pouta 8y ago
Do you have some to assign right now? :)

by DrNuke 8y ago

nightski 8y ago

I was maybe a bit too snarky, apologize if so. But the crux of it is that his model never achieved > 50% accuracy. It was never necessary because he already got the job. But the thing is, that is the hard part. Closing those gaps in not just accuracy, but also generalization is the data science portion of the task (and requires much more knowledge than what is demonstrated by this blog post - although they could of left out a lot of detail). They make it seem like if he just had a little bit more time this would of been straightforward. But I am not sure about that. I am all for giving junior practitioners a chance. But this is like hiring an english major for aerospace engineering because they built a model airplane in my opinion. But maybe I vastly underestimate the amount of extremely low hanging fruit out there for ML projects.

DrNuke 8y ago
You’re welcome and not snarky at all. I am not sure you work with commercially-driven ML projects? It really depends on the metrics only. I can’t trust a ML black box for cancer screening (both false negatives and false positives have a big cost) or complex industrial failures (stopping a plant starts a lot of expensive compensating manouvres) but nobody sane in his mind has a real problem with 88, 90 or 92% accuracy for online retail recommenders (all the field is a kind of magic beyond a certain baseline and money never earned is not money lost from a bookkeeping perspective) or language translation (which is still human-proofed wherever it gets legal value of any kind). Hope I made my point clearer. Cheers.

pouta 8y ago

Do you have some to assign right now? :)