Distinguishing between Statistical Modeling and Machine Learning

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Editor’s note: This article will serve as a great overview. After reading it, we recommend listening the the podcast at the bottom, it may just broaden your understanding.


If you are looking for it, here is one framework to distinguish statistical modeling from machine learning, and it is based on the desire for interpretability.

In summary, if you want to understand a model, and value interpretability, you are interested in model inference, and therefore are performing statistical modeling. If all you care about are accurate predictions, and not the interpretability of a model, you are doing machine learning.

statistical modeling <- interested in model inference/understanding a model
machine learning <- interested in accurate predictions

Examples

Take two examples. If you want to understand a medical condition, say, heart disease, based on features like blood pressure, smoking, etc, you will build simple/parsimonious models, such as:

heart disease <- elevated blood pressure + chronic smoking + a few other predictors

You want to take action (treat the heart disease) so you study how it is associated with some other things, like blood pressure. You build a model that you can understand, and interpret.

On the other hand, if you are interested in text processing to convert pictures of street signs into computer-readable text, you may not care about how the model really works. All you want is a correct prediction: is this letter a “P” or a “B”?

Is this letter a "P"? <- bunch of data

As long as the model has good predictive power, you will be happy, and won’t care if the data is inter-correlated (collinear) or not, or how many (possibly transformed) predictors you have, whether 10 or 10,000.

Explanation

In the heart condition example, you are doing inference with simpler, interpretable models, and this is statistical modeling. In the text classification example, the model isn’t as important as its predictive power, and you are doing machine learning.

In terms of actual methods, to understand heart disease or another condition, you may build a logistic regression model with a few predictors. To classify text or an image, you may implement gradient boosting descent or some complicated neural network.

Caveats

To be sure, there is overlap among these two approaches. You may want to make predictions in the statistical modeling framework, and for sure you can. Also, logistic regression is often mentioned as a machine learning model.

Still, the framework described here is just that, a framework or a model, and we all (should) know that in certain ways, “all models are wrong.” But to complete the thought, “… but some are useful.” *

Summary and acknowledgment

In summary:

statistical modeling <- perform inference based on interpretable models
machine learning <- make accurate predictions from heaps of data. Interpretability not important

This framework was partly inspired by an informal statement from Hadley Wickham.**

Let me know what you think

If you or someone else have a different take, or you disagree with something in the above, I am all ears. Let me know here.

References

*You can read here about the source of this quote.

**Essentially, what he says is that “machine learners care about making really accurate predictions” and “statisticians generally … want to understand what is going on”. See this podcast from March 31, 2016, around 24:20:


Originally posted at pavopax.github.io. Paul Paczuski is on Twitter and Github