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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

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 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.

Buttons840 8y ago
> Classical statistics is very much focused on explaining a dataset, whereas ML is very much focused on making future predictions. True enough. Could you slightly change this and say statistics is about understanding the past and ML is about predicting the future? The only way to predict the future is to understand the past, or be very lucky.
- Spivak 8y ago
  One branch of statistics is about understanding the past. Statisticians are very much interested in predicting the future and have methods that look nothing like ML.
basch 8y ago
>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.
titanix2 8y ago
> 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).

Buttons840 8y ago

> Classical statistics is very much focused on explaining a dataset, whereas ML is very much focused on making future predictions. True enough. Could you slightly change this and say statistics is about understanding the past and ML is about predicting the future? The only way to predict the future is to understand the past, or be very lucky.

Spivak 8y ago
One branch of statistics is about understanding the past. Statisticians are very much interested in predicting the future and have methods that look nothing like ML.

basch 8y ago

>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.

titanix2 8y ago

> 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).