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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

#31

Post by basch »

wodenokoto wrote: post #20

I'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 algo…

>future predictions.

But what is a future prediction really? You have a model and a dataset (possibly generated by previous input to the model), and then you ask it "IF x then what, IF y then what."

It's the exact same thing as turning data into an approximate line, and then inputting data into the function to predict the output value, within a certain confidence.

Re: Machine learning is not just glorified statistics

#32

Post by Spivak »

chubot wrote: post #27
sideshowb wrote: post #18

Earlier quoted context omitted.

That's the difference between inference and prediction. Both statistics and machine learning can do either of those things.

Can you give some examples of both?

Statistics being used for prediction: https://fivethirtyeight.com/features/how-our-march-madness-p...

Statistics being used for inference: https://fivethirtyeight.com/features/stephen-curry-is-the-re...

Re: Machine learning is not just glorified statistics

#33

Post by MikeLui »

tempodox wrote: post #11
RedComet wrote: post #4

Yes, it is.

No, it's worse. It's statistics on steroids used by people who don't have a clue about statistics.

"No, Computing is not just glorified math". At the end of the day, yes it is, but I can understand the sentiment of those who don't need/care to look at it any deeper.

Re: Machine learning is not just glorified statistics

#34

Post by cwyers »

Machine Learning is not just statistics. David Donoho spells out the history of the whole thing in his 50 Years Of Data Science:

https://courses.csail.mit.edu/18.337/2015/docs/50YearsDataSc...

The clearest statement of the difference I've found is Leo Breiman's "Statistical Modeling: The Two Cultures:"

https://projecteuclid.org/download/pdf_1/euclid.ss/100921372...

The abstract has a succinct explanation:

> Abstract. There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems. Algorithmic modeling, both in theory and practice, has developed rapidly in fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools.

The "stochastic data model" camp covers most of what people think of when they think of traditional stats -- everything from OLS through to more sophisticated techniques. Things like neural networks and random forests are algorithmic techniques that make no assumptions about or conclusions about the distribution of the underlying data.

Re: Machine learning is not just glorified statistics

#35

Post by autokad »

basch wrote: post #6
pathseeker wrote: post #3

Mostly 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…

> "Training a model is skipping to executing the function, without first learning how to write it. Is training a model even a feat worth bragging about?"

do you really think its easy? go do kaggle competitions. if its really easy you should just be able to run a few models and quit your day job.

ML and statistics are very much the same and just as different. maybe its true about ML people as well, but statisticians have a dogma. they think there is a right model, and they came up with it. ML practitioners generally think they have a more useful model than what was previously done.

I had a PHD in statistics try to do deep learning on images. they never got far because they could not get over the idea of having more parameters than observations. I had stats people ask me what is the r^2 on the results of an xgboost model, which the best answer I could give them was that the mean was not a good representation of the data therefore any r^2 number could not be trusted, to which they hated.

Re: Machine learning is not just glorified statistics

#36

Post by stonogo »

typomatic wrote: post #19
stonogo wrote: post #8

Earlier quoted context omitted.

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?

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.

Re: Machine learning is not just glorified statistics

#37

Post by lottin »

This sentence sums it up perfectly:

> In many cases, these algorithms are completely useless in aiding with the understanding of data and assist only in certain types of uninterpretable predictive modeling.

Statistics is a crucial component of the scientific method. It is the tool with which scientists check whether their theories agree with empirical evidence.

Machine learning is about building mathematical models that are apparently "right", in the sense that they have predictive power, but that don't necessarily improve our understanding of the data-generating processes involved.

Re: Machine learning is not just glorified statistics

#38

Post by basch »

autokad wrote: post #35
basch wrote: post #6

Earlier quoted context omitted.

"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…

> "Training a model is skipping to executing the function, without first learning how to write it. Is training a model even a feat worth bragging about?" do you really think its easy? go do kaggle competitions. if its really easy you should just be able to run a few models and quit your day job. ML and statistics are very much the same and just as different. maybe its true about ML people as well, but statisticians h…

it was a bit of hyperbole.

Re: Machine learning is not just glorified statistics

#40

Post by titanix2 »

wodenokoto wrote: post #20

I'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 algo…

> Or that NLP is not neither machine learning or statistics, because it is about language.

Except NLP is actually not machine learning. Some NLP tasks use machine learning but a lot of NLP is not ML-based (for instance rule-based TA systems).

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