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Machine learning is not just glorified statistics

towardsdatascience.com

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Re: Machine learning is not just glorified statistics

#51

Post by thousandautumns »

cwyers wrote: post #49
thousandautumns wrote: post #48

Earlier quoted context omitted.

The premise of the abstract is flawed though. Who says statisticians aren't using algorithmic models? Perhaps the author doesn't but random forests, clustering, PCA, gaussian processes, and even neural networks are standard fare for many statisticians.

I mean, the author certainly used random forests, seeing as he invented them: https://www.berkeley.edu/news/media/releases/2005/07/07_brei...

I can't help but notice he died in 2005. Perhaps the premise of the two cultures was more relevant a few decades ago, but in my more recent experience, its absolutely not the case.

Re: Machine learning is not just glorified statistics

#52

Post by srean »

thousandautumns wrote: post #46

I don't love the term 'mansplaining', but if there is a term that describes essentially the same idea but in the context of academic fields, its exactly how I would describe the central thesis of this blog post and a trend I've encountered frequently in the last couple years. There is a rising tide of CS people who have just latched onto the hype of data science, and now go around letting statisticians know that no,…

> It certainly comes as a shock to all of the statisticians in the world who have indeed been working on these types problems for a long time now

Yup on high dimensional data of dimension as fantastic as 12. I feel bad for them though but they have only themselves to blame -- got too comfortable within their small world and lost touch of what the next set of interesting problems are.

Its only after getting kicked in the nuts that I see a course correction and that's enriching both ML as well as Stats.

If one computes stats on 600 data points with 10 dimensions and feels king of the hill, they can continue, but there is likelihood that some one else will be eating your lunch and you will be left behind. Sadly enough, this has already happened and is quite evident if one steps out of the stats bubble. Statistics could have been what machine learning and datamining is now, been the main driving force, the owner of initiative. On the contrary other communities are using statistics and probability motivated approaches but engineering them well to grab (funding) attention, well deserved in my opinion. It is them who got the ball rolling again.

https://news.ycombinator.com/item?id=17687303

Re: Machine learning is not just glorified statistics

#53

Post by justfor1comment »

I have to frequently work with Data Scientists to learn their ML models and scale them in our production environments. A lot of times the models are not even statistics, it's just linear algebra. They have a tendency to go for approximate algorithms when an exact algorithm could be written. I largely blame the company for this and not the data scientists. Every company wants to be in the ML game but may not have the volume and variety of data to warrant a use case. When your favorite hammer is ML then every data problem is a nail.

Re: Machine learning is not just glorified statistics

#54

Post by sideshowb »

thousandautumns wrote: post #43
chubot wrote: post #13

Here'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…

I completely disagree, and this is a notion that seems to have become popular but is completely off base. For some reason, people seem to believe that statistics is for inference, and machine learning is for prediction, as if statistics hasn't been concerned with matters of prediction for hundreds of years. Machine learning is closer to being a subfield of statistics, in which methods tend to be non-parametric, have…

I think you're on point except for cynicism in your last paragraph. Machine learning came from genuinely novel research into ai. Over time the connections between ml and statistics became apparent, ml tightened up it's statistical rigour, stats took on some new techniques, and data science was born.

I'm sure some have played the machine learning card cynically, as with any buzzword, but don't throw out baby with bathwater.

Re: Machine learning is not just glorified statistics

#55

Post by thousandautumns »

sideshowb wrote: post #54
thousandautumns wrote: post #43

Earlier quoted context omitted.

I completely disagree, and this is a notion that seems to have become popular but is completely off base. For some reason, people seem to believe that statistics is for inference, and machine learning is for prediction, as if statistics hasn't been concerned with matters of prediction for hundreds of years. Machine learning is closer to being a subfield of statistics, in which methods tend to be non-parametric, have…

I think you're on point except for cynicism in your last paragraph. Machine learning came from genuinely novel research into ai. Over time the connections between ml and statistics became apparent, ml tightened up it's statistical rigour, stats took on some new techniques, and data science was born. I'm sure some have played the machine learning card cynically, as with any buzzword, but don't throw out baby with bath…

I absolutely am not disparaging the actual research in machine learning borne from academics studying CS and AI. Rather, I'm referring to the seemingly recent trend by people to insist on severing all ties between statistics and machine learning. And it consistently seems to be made in articles like the one above written by authors who almost seem happy to insist they know nothing, and need to know nothing, regarding statistics.

Perhaps I am being too cynical, but its hard not to see ulterior motives given the ridiculous amounts of hype regarding machine learning, the innumerable boot camps, and to-be data science influencers.

Re: Machine learning is not just glorified statistics

#56

Post by thousandautumns »

srean wrote: post #52
thousandautumns wrote: post #46

I don't love the term 'mansplaining', but if there is a term that describes essentially the same idea but in the context of academic fields, its exactly how I would describe the central thesis of this blog post and a trend I've encountered frequently in the last couple years. There is a rising tide of CS people who have just latched onto the hype of data science, and now go around letting statisticians know that no,…

> It certainly comes as a shock to all of the statisticians in the world who have indeed been working on these types problems for a long time now Yup on high dimensional data of dimension as fantastic as 12. I feel bad for them though but they have only themselves to blame -- got too comfortable within their small world and lost touch of what the next set of interesting problems are. Its only after getting kicked in…

Your response is so entirely off base I don't know where to begin.

> Yup on high dimensional data of dimension as fantastic as 12

Who says that has been the limit of classical statistics.

> If one computes stats on 600 data points with 10 dimensions and feels king of the hill, they can continue, but there is likelihood that some one else will be eating your lunch and you will be left behind.

Again, why do you have this impression? You clearly have no experience in the field if this is what you think statistics is. Unless your intent is to simply construct strawman arguments.

> Statistics could have been what machine learning and datamining is now, been the main driving force, the owner of initiative.

This is entirely based on the assumption that machine learning and datamining and statistics are distinct and separate, which isn't the case and is my entire point.

> On the contrary other communities are using statistics and probability motivated approaches but engineering them well to grab (funding) attention, well deserved in my opinion. It is them who got the ball rolling again.

Seriously, wtf are you talking about?

Re: Machine learning is not just glorified statistics

#57

Post by srean »

thousandautumns wrote: post #56
srean wrote: post #52

Earlier quoted context omitted.

> It certainly comes as a shock to all of the statisticians in the world who have indeed been working on these types problems for a long time now Yup on high dimensional data of dimension as fantastic as 12. I feel bad for them though but they have only themselves to blame -- got too comfortable within their small world and lost touch of what the next set of interesting problems are. Its only after getting kicked in…

Your response is so entirely off base I don't know where to begin. > Yup on high dimensional data of dimension as fantastic as 12 Who says that has been the limit of classical statistics. > If one computes stats on 600 data points with 10 dimensions and feels king of the hill, they can continue, but there is likelihood that some one else will be eating your lunch and you will be left behind. Again, why do you have th…

> Who says that has been the limit of classical statistics.

The professors writing the grants. No not the limit of statistical methods per se but the limit of what they want to consider. Yeah I used to get involved in the review on rare occasions.

> You clearly have no experience in the field if this is what you think statistics is.

Does 18 years count ? I may be wrong about this but you sound like a newish grad student in statistics. If that is true I go back a bit more than you do and know about the state of affairs at the stats departments, their funding/projects/budget woes. I feel glad that the statistics departments got shaken a bit by ML and datamining for statistics to try and become relevant again.

If you follow the link in my parent comment you will see a vigorous argument (presumably by a student of statistics) that there is no reason why statistical packages even need to support 64 bits. This kind of thinking was pervasive, thankfully things are a bit better now and that would not have happened on its own.

Re: Machine learning is not just glorified statistics

#58

Post by tabtab »

mrfredward wrote: post #16

I 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, ra…

It may not be an "application of statistics" in the direct sense, but that depends on how one defines statistics, which brings one back to the original problem.

Statistics and ML often have similar goals, but ML emphasizes computational efficiency over trace-able accuracy. Thus, I view each field as having different weights on the same sub-goals.

Re: Machine learning is not just glorified statistics

#59

Post by cwyers »

thousandautumns wrote: post #51
cwyers wrote: post #49

Earlier quoted context omitted.

I mean, the author certainly used random forests, seeing as he invented them: https://www.berkeley.edu/news/media/releases/2005/07/07_brei...

I can't help but notice he died in 2005. Perhaps the premise of the two cultures was more relevant a few decades ago, but in my more recent experience, its absolutely not the case.

There is certainly cross-pollination between the two, and practitioners these days often adopt both. But the snide "machine learning is just statistics practiced by people who don't know what they're doing" comments you tend to see on HN ignore that machine learning was something that sprung up (largely in CS departments) to address challenges that statistics departments weren't addressing.

Re: Machine learning is not just glorified statistics

#60

Post by typomatic »

stonogo wrote: post #36
typomatic wrote: post #19

Earlier quoted context omitted.

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?

Not merely a suggestion; I'm stating outright that when someone's career (both the value of an acquired scholarly degree and the earned income within the field) depends on a field being regarded as a valid specialization, that person is going to put effort into reinforcing that perceived validity.

Oh okay! I thought you were suggesting the opposite (which is nonsense). Thanks!
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