Re:Machine Learning Often a Complicated Way of Replicating S...
by stanfordkid on Monday December 17, 2018 @03:17PM
(#18701801)
Pretty banal article. TLDR: A very complicated model will fit simple data, as will a simple model. Modern ML/AI has nothing to do with 1D timeseries forecasting.
[ Parent
| on HN ]
Re:Machine Learning Often a Complicated Way of Replicating S...
by cs702 on Monday December 17, 2018 @03:29PM
(#18701883)
Was about to write the same thing.
What the heck is this doing on the front page?
Re:Machine Learning Often a Complicated Way of Replicating S...
by henning on Monday December 17, 2018 @04:34PM
(#18702415)
There have been thousands of papers and many books written on the subject of using machine learning for time series forecasting and an unknown amount of money traded/invested with it. Most of them cast trading as a time series forecasting problem where performance is measured by mean square error. They usually then assume that would lead to profitable trading.
You don't get to define what "modern machine learning" is any more than I do.
The application of complex models to higher-dimensional domains have problems of their own. Both the successes and failures are worth writing and thinking about.
The post is an important reminder about simplicity and complexity in modeling. That's why I submitted it.
Re:Machine Learning Often a Complicated Way of Replicating S...
by stanfordkid on Tuesday December 18, 2018 @01:44PM
(#18709290)
I don't understand why I'm being downvoted for this opinion -- the topic of overfitting gets beaten to death by the 3rd week of every intro AI/statistics/ML class out there. I didn't find it very worthy of the frontpage (or a blog post).