# Multivariate statistics vs machine learning?

Are multivariate statistics and machine learning solving the same problems? I saw that their books are about the same topics, so I have the impression that they are solving the same problems and probably using the same methods.

What are their relations and differences?

Can machine learning solve the same problems in univariate statistics?

Thanks!

I think this is a great question, and not an easy one to answer. I conceptualize that machine learning encompasses a lot of multivariate statistics, because many of the common techniques in multivariate analysis (ordination and clustering, for instance) use unsupervised learning algorithms. With people like me who aren't that concerned about the computer side of things, a lot of this stuff appears to be "under the hood", and I usually am focused more on how ordination relates as an extension of regression. But it cannot be ignored that the computer is doing some pretty advanced searching for patterns that I am not responsible for.

Then there are supervised learning techniques in machine learning outside the realm of regular multivariate analysis. For instance, if you want to predict what categories some new object would go into based upon some of its variable's values, then you can train the algorithm to a bunch of objects that you know the classification of and then set the algorithm on classifying the new object. This is clearly not a multivariate statistics technique, and I tend to think of this when I think ofmachine learning because it involves that process of communicating the success or failure of a search to the system. Then this is where machine learning starts to overlap with AI, and things quickly get completely out of my depth...

In the end, I do agree with the second answer on this thread that machine learning emphasizes prediction, whereas statisics in general is concerned with inference - but again, this is broad strokes stuff and not always going to be true.

• Thanks. "Then there are supervised learning techniques in machine learning outside the realm of regular multivariate analysis." Discriminatory anlysis is part of Multivaraite statistics, and is classification, isn't it?
– Tim
May 24, 2014 at 5:35
• Ordination refers to techniques like NMDS, PCA, CCA, etc. It involves compressing high-dimensional data into linear combinations to reduce redundant variables and help look for dominant patterns. You may have caught me out on discriminant function analysis - this is not a technique I use and had sort of forgotten about :) I would say this also probably a machine learning technique. It may be harder for me to come up with machine learning techniques that are not multivariate analysis since I don't use it much - hopefully more answers or other threads can help May 24, 2014 at 5:42
• thanks. do you think that machine learning can solve the same problem as univariate statistics?
– Tim
May 24, 2014 at 5:44
• I'm sure it can. Machine learning really just refers to a method of solving problems - teaching a system to do something. I don't see why this would be restricted to multivariate data. May 24, 2014 at 5:57

I think Machine learning is very specific class of powerful learning models while Multivariate Statistics or Statistics in general is a framework. In statistics you deal with all kinds of things related to measurements, summarization and uncertainties (examples are hypothesis testing, power, confidence interval, etc...). Of course, it is inevitable to have some machine learning models in Multivariate Statistics because it is a way to summarize data but that doesn't diminish the field of Machine Learning. Remember that you can also view all sciences as model making endeavour but that doesn't diminish the value of those sciences and the effort given to them. Machine Learning is wide enough to be considered a field on its own just like any science.

There are some overlap but they don't necessarily solve the same problems in general just like Statistician and Scientist don't have similar problems.