Modelling with more variables than data points I'm fairly new to Machine Learning/Modelling and I'd like some background to this problem.
I have a dataset where the number of observations is $n<200$ however the number of variables is $p\sim 8000$.
Firstly does it even make sense to consider building a model on a dataset like this or should one consider a variable selection technique to start with such as ridge regression or Lasso? I've read that this situation can lead to over-fitting. Is that the case for all ML techniques or do some techniques handle this better than others? Without too much maths a simple explanation on why the maths start to breakdown for $p>n$ would be appreciated.
 A: There are many solutions this problem: find three terms whose sum is equal to $3$: $3=7-3-1$, $3=1234-23451+22220$, for instance. Here, the number of observations is one ($n=1$) and $p=3$.
In mathematics, a useful concept is that of overdetermined systems (and their converse, underdetermined systems). Key features from the previous wikipedia pages:

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*"overdetermined if there are more equations than unknowns" and "is almost always inconsistent (it has no solution) when constructed with random coefficients. However, an overdetermined system will have solutions in some cases"

*"underdetermined if there are fewer equations than unknowns" and "In general, an underdetermined system of linear equations has an infinite number of solutions, if any. However, in optimization problems that are subject to linear equality constraints, only one of the solutions is relevant, namely the one giving the highest or lowest value of an objective function."

Without additional assumptions, one has difficulties finding a very meaningful solution. In practice, you may assume that you have no more that two non-zero terms (sparsity hypothesis), or you can constrain them to be positive (positivity hypothesis). In such a case, you end up with ordered triplets like $(3, 0, 0)$ or $(2, 1, 0)$, a reduced set which you can explore as potential "practical" solutions which shall be tested or probed. You can reduce the space of search, by imposing all variables to be equal (a kind of zero-degree parametric model). Then, $(1, 1, 1)$ would be the solution.
This is what penalized regression (like lasso or ridge) is meant for: find a manageable subset of "simpler" solutions, potentially more natural to some extent.  They use the law of parsimony, or Ockham's razor, for which if two models explain the observation  which the same precision, it can be wisest to choose the more compact in terms of, for instance, the number of free parameters. One does not really "explain" useful relationship between variables with too involved models.
A quote attributed to John von Neumann illustrates this context:

With four parameters I can fit an elephant, and with five I can make
him wiggle his trunk.

A: It's certainly possible to fit good models when there are more variables than data points, but this must be done with care.
When there are more variables than data points, the problem may not have a unique solution unless it's further constrained. That is, there may be multiple (perhaps infinitely many) solutions that fit the data equally well. Such a problem is called 'ill-posed' or 'underdetermined'. For example, when there are more variables than data points, standard least squares regression has infinitely many solutions that achieve zero error on the training data.
Such a model would certainly overfit because it's 'too flexible' for the amount of training data. As model flexibility increases (e.g. more variables in a regression model) and the amount of training data shrinks, it becomes increasingly likely that the model will be able to achieve a low error by fitting random fluctuations in the training data that don't represent the true, underlying distribution. Performance will therefore be poor when the model is run on future data drawn from the same distribution.
The problems of ill-posedness and overfitting can both be addressed by imposing constraints. This can take the form of explicit constraints on the parameters, a penalty/regularization term, or a Bayesian prior. Training then becomes a tradeoff between fitting the data well and satisfying the constraints. You mentioned two examples of this strategy for regression problems: 1) LASSO constrains or penalizes the $\ell_1$ norm of the weights, which is equivalent to imposing a Laplacian prior. 2) Ridge regression constrains or penalizes the $\ell_2$ norm of the weights, which is equivalent to imposing a Gaussian prior.
Constraints can yield a unique solution, which is desirable when we want to interpret the model to learn something about the process that generated the data. They can also yield better predictive performance by limiting the model's flexibility, thereby reducing the tendency to overfit.
However, simply imposing constraints or guaranteeing that a unique solution exists doesn't imply that the resulting solution will be good. Constraints will only produce good solutions when they're actually suited to the problem.
A couple miscellaneous points:


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*The existence of multiple solutions isn't necessarily problematic. For example, neural nets can have many possible solutions that are distinct from each other but near equally good.

*The existence of more variables than data points, the existence of multiple solutions, and overfitting often coincide. But, these are distinct concepts; each can occur without the others.

