# Summary: Machine learning is not nonparametric statistics.

October 14, 2021

*Machine learning is not nonparametric statistics. In fact, most of statistics seems to pursue very different questions than those studied in machine learning.*

- By prediction, I mean the general problem of leveraging regularity of natural processes to guess the outcome of yet unseen events.
- Indeed, for all of the talk about neuromorphic deep networks with fancy widgets, most of what machine learning does is try to find computer programs that make good predictions on the data we have collected and that respect some sort of rudimentary knowledge that we have about the broader population.
- Balancing representation, optimization, and generalization gets complicated quickly, and this is why we have a gigantic academic and industrial field devoted to the problem.
- Even generalization, which is usually studied as a statistical phenomenon, can be analyzed in terms of the randomness of the sampling procedure with no probabilistic modeling of the population.
- Engineering such prior knowledge into appropriate function classes and optimization algorithms form the art and science of contemporary machine learning.
- Another common refrain from statistics is that model complexity must be explicitly constrained in order to extrapolate to new data, but this also does not seem to apply at all to machine learning practice.
- In the next post, I’ll discuss an early example of this divergence between machine learning and statistics, describing some of the theoretical understanding of the Perceptron in the 1960s and how its analysis was decidedly different from the theory advanced by statisticians.

**Read the complete article at:** www.argmin.net