Science of Machine Learning (was Machine Learning , some thoughts)

Philippe Ameline philippe.ameline at
Tue Jul 3 06:21:30 EDT 2018

Le 02/07/2018 à 11:31, Bert Verhees a écrit :

> On 30-06-18 17:16, Philippe Ameline wrote:
>> (improperly labeling images or adding images of objects that are not
>> plants) could probably make the whole app plainly crappy.
> Of course Philippe, but that would be vandalism. Most sensible people
> don't do that when they stand behind the goal, and a little bit of
> dirt, therefor it is Machine Learning, it can filter it out. It is
> part of the learning process.

If a culture of data quality is properly installed, then it is possible
to name improper use "vandalism".
In medicine, since such a culture has never existed, we could name it
"don't carisme", "no time for thisisme" or "was never thaughtisme".

My point, and what the paper I previously pointed out explains, is that
trying to get something out of machine learning in a domain of poor data
quality is a modern kind of magic thinking.
It just means that any such project should first organize for data
quality as a first step.

When considering it in hindsight, it makes sense since machine learning
involves statistics and data quality is paramount in this domain.

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