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Earlier quoted context omitted.
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...
“One of our assigned projects was to implement and train a Wasserstein GAN in TensorFlow. At this point, I had taken only an introductory statistics class that was a required general elective, and then promptly forgotten most of it. Needless to say, my statistical skills were not very strong. Yet, I was able to read and understand a paper on a state-of-the-art generative machine learning model...Yet, if you had asked me, or most of the students in that class, how to calculate the variance of a population, or to define marginal probability, you likely would have gotten blank stares. ”
I agree that the GAN might be implementable without much stats knowledge, but I would be very surprised if someone who did not know population variances and marginal probabilities would be able to follow the Wasserstein GAN paper. Just see for yourself: https://arxiv.org/abs/1701.07875
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…
Machine learning is closer to being a subfield of statistics, in which methods tend to be non-parametric, have an emphasis on automation, and tend to require (but not necessarily) large amounts of data.
I think the entire reason for this notion is due to CS folks wanting in on the field of predictive analytics, but not wanting to admit that they don't have even minimal understanding of statistics or probability theory. But if they call it machine learning and insist enough that it is a completely different discipline, then they can feel better about themselves and their models that they threw together.
Earlier quoted context omitted.
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...
As others have pointed out, in statistics the goal is understanding something. Once you have understood it you can predict its behaviour. However the reverse is not true.
Earlier quoted context omitted.
As others have pointed out, in statistics the goal is understanding something. Once you have understood it you can predict its behaviour. However the reverse is not true.
This isn't true though. There are ton's of uninterpretable model methodologies in classical statistics that have little to no ability to allow for understanding but are aimed entirely at accurate predictions. Where is this narrative that statistics is only interested in understanding coming from?
You may have heard of this guy called Fisher he might like to have a few words with you. He says he fathered modern Stats, that he connected what was a bag of recipes to math. Many seem to agree [0] despite the fact he does not seem to be the most pleasant bloke around. The British queen seemed awfully impressed with him though, YMMV.
[0] "a genius who almost single handedly created the foundations for modern statistical science" -- Hald, Anders, A History of Mathematical Statistics.
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.…
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.…
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.
https://www.berkeley.edu/news/media/releases/2005/07/07_brei...
Earlier quoted context omitted.
This isn't true though. There are ton's of uninterpretable model methodologies in classical statistics that have little to no ability to allow for understanding but are aimed entirely at accurate predictions. Where is this narrative that statistics is only interested in understanding coming from?
> Where is this narrative that statistics is only interested in understanding coming from? You may have heard of this guy called Fisher he might like to have a few words with you. He says he fathered modern Stats, that he connected what was a bag of recipes to math. Many seem to agree [0] despite the fact he does not seem to be the most pleasant bloke around. The British queen seemed awfully impressed with him though…
1. R.A. Fisher isn't the Almighty Statistical God just because he did laid a lot of foundations in early statistics.
2. Fisher has a long history of seriously stupid personal-beliefs including but not limited to: refuting anything and everything relating to Bayesian statistics, attempting to discredit the studies done showing a link between smoking and lung cancer, and advocating for eugenics and the idea of superiority/inferiority between races.
So just because Fisher may have claimed something doesn't make it so.