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> SVM, linear regression, naive Bayes When I studied ML in 2012, the very first course started with naive Bayes and went one from there. A decade after being a
by mickael-kerjean 3y ago
> SVM, linear regression, naive Bayes
When I studied ML in 2012, the very first course started with naive Bayes and went one from there. A decade after being away, I see a lot of people around me starting with neural nets to train a model that naive Bayes would be plenty enough for and never heard about naive Bayes. Is that only my experience?
- yCombLinks 3y agoAny data that is small enough to quickly iterate on for learning is small enough to use a simpler approach. The point is learning the techniques.
- owlbite 3y agoAs with any super-hyped technology, people want to use the latest and greatest to solve it, even when traditional methods are cheaper, easier, more reliable and more accurate. See also: crypto.
- PeterisP 3y agoIt's useful to do learning on small toy problems for ease of debugging and speed of training, so if you want to learn how to apply a powerful technique, you're pretty much inevitably going to start learning it on something for which it's absolutely ineffective overkill. E.g. a common start for learning neural nets is a task like XOR which can be solved with literally a single machine instruction instead of training a ML model for it. But also there are many tasks for which naive Bayes works, but a NN solution can be much more accurate if you're okay with it being also much more compute-intensive. E.g. things like sentiment analysis or simple spam filters are often used as a demonstration of naive Bayes, but you can do much better with more powerful models.