Yes, it is.
No, it's worse. It's statistics on steroids used by people who don't have a clue about statistics.
Machine learning is not just glorified statistics
11–20 of 64 posts
Re: Machine learning is not just glorified statistics
#12"..I get it — it’s not fashionable to be part of the overly enthusiastic, hype-drunk crowd of deep learning evangelists."
I think it is very fashionable. The people who are not part of this crowd are perhaps older and boring like me.
Re: Machine learning is not just glorified statistics
#13Here's one way I think of it: in statistics vs. machine learning, there's a difference in your goals, which is reflected in a difference in your models.
- In statistics, the goal is to explain something. The models have few variables, and each variable should mean something, like the influence of the person's age or sex on the outcome.
- In machine learning, the goal is to make something work. This is apparently better done with millions of variables (neural network weights), and each variable is opaque and means nothing by itself.
He hints at this distinction in the blog post but it's not entirely clear.
Re: Machine learning is not just glorified statistics
#14Re: Machine learning is not just glorified statistics
#15Mostly just arguing that it's special and not 'statistics' because it's statistics being calculated at a much larger scale in automated fashions. Seems like unnecessary gatekeeping just for the purpose of inflating egos.
His bio indicates he does 'ML @ Harvard'. I would imagine there's more than just ego riding on this.
Indeed, he should have attached ©, ®, and ™ to that phrase.
Re: Machine learning is not just glorified statistics
#16I would consider machine learning to be an application of statistics, in much the same way that mechanical engineering is an application of physics. The foundations of mechanical engineering are rooted in physical concepts, but mechanical engineers have formulas for all sorts of things, like calculating the fatigue life of gears, that you can't get from pure physics because they are empirically derived curve fits, rather than natural laws. The science is more pure, but the application of it is what's powerful in the real world.
So in a sense I agree that "machine learning is not statistics," but I strongly disagree with the tone of the article, which is "we're better than statistics." Don't shit on the shoulders you're standing on.
Re: Machine learning is not just glorified statistics
#17Mostly just arguing that it's special and not 'statistics' because it's statistics being calculated at a much larger scale in automated fashions. Seems like unnecessary gatekeeping just for the purpose of inflating egos.
"I havent learned classical statistics, but I did take a machine learning course, so what I did must not have been that thing I dont know much about." >Throughout the class, my fellow students and I successfully trained models On top of that, arguing that training models is some sort of contribution to the field? In High School Statistics, we were taught how to do everything by hand, with arithmatic. Then after we de…
Or c) you didn't "develop a deep understanding of cutting-edge ML concepts."
Re: Machine learning is not just glorified statistics
#18Here's one way I think of it: in statistics vs. machine learning, there's a difference in your goals, which is reflected in a difference in your models. - In statistics, the goal is to explain something. The models have few variables, and each variable should mean something, like the influence of the person's age or sex on the outcome. - In machine learning, the goal is to make something work. This is apparently bett…
That's the difference between inference and prediction. Both statistics and machine learning can do either of those things.
Re: Machine learning is not just glorified statistics
#19Mostly just arguing that it's special and not 'statistics' because it's statistics being calculated at a much larger scale in automated fashions. Seems like unnecessary gatekeeping just for the purpose of inflating egos.
His bio indicates he does 'ML @ Harvard'. I would imagine there's more than just ego riding on this.
Your suggestion is that someone who did a degree specifically in machine learning at Harvard would be less likely to be defending their discipline out of a sense of ego?
Re: Machine learning is not just glorified statistics
#20I've been meaning to ask if "townrdsdatascience" is a serious website, but I guess this answers it.
It seems in order for the argument here to make sense, you have to say that predictive modelling is outside the field of statistics, and that "a class of computational algorithms" (presumably including classic ML algorithms such as decision trees, random forest and support vector machines) are also not statistical algorithms.
The author gets close to having a point when saying that in reinforcement learning you may not even have a dataset, so what is there to make statistics on? Well, you make statistics on the generated data.
I think he is right once he starts talking about approach and not knowing "variance of a population, or to define marginal probability" is not necessary to perform ML. I mean, you can perform ML without knowing what variance is, just as well as you can perform psychology experiments without knowing the variance, but I get his point:
Classical statistics is very much focused on explaining a dataset, whereas ML is very much focused on making future predictions. And you can combine and build a lot of predictive models, without the knowledge of the other half of statistics, and vice versa. But this argument is like saying topology isn't math because it is about shapes and not numbers. Or that NLP is not neither machine learning or statistics, because it is about language.