
Ask HN: When/Where to Not Use Machine/Deep Learning? - bhnmmhmd
It&#x27;s good to stay away from the hype as much as possible. In what applications do you think using ML is not necessary, or worse still, a bad idea?
======
PredictorY
Most often, "machine learning" refers specifically to supervised learning.
Generally, such technology requires an historic set of examples of sufficient
volume and quality (what this means varies greatly from problem to problem)
from which to learn. Some problems do not come with a reasonable set of
training data, and none is forthcoming: these situations are a poor choice for
machine learning. Each of these factors makes example data less desirable:
candidate explanatory variables which are few in number or highly redundant,
missing values and poor measurement. All of the challenges one faces in
traditional statistical data collection and sampling apply.

Some problems are not learning problems at all. Retail companies with poor
management and consequent poor service, for instance, need machine learning
less than they need competent management.

