# In linear regression, we have 0 training error if data dimension is high, but are there similar results for other supervised learning problems?

I tried posting this question on Cross Validated (the stack exchange for statistics) but didn't get an answer, so posting here:

Let's consider a supervised learning problem where $$\{(x_1,y_1) \dots (x_n,y_n)\} \subset \mathbb{R}^p \times \mathbb{R}$$ where $$x_i \sim x$$ are iid observations/samples, and $$y_i \sim y$$ are iid response variables. $$y$$ can be either continuous(regression) or discrete random variable (classification). To simplify things, you can treat the $$x_i, y_i$$'s below as individual input and output, as opposed to random vectors/variables.

We know that if the learning problem at hand is linear regression, then $$p \ge n-1$$ is sufficient to guarantee an interpolation - i.e. the hyperplane in $$\mathbb{R}^{p+1}$$ passing through (and not passing near) all the points $$\{(x_1,y_1) \dots (x_n,y_n)\} \subset \mathbb{R}^p \times \mathbb{R}$$, thereby giving us an exact zero training error (and not a small, positive training error).

My question is: are there such lower bound on the data dimension, a lower bound that's a function of the sample size $$n,$$ that ensures zero training errors when the supervised learning problem at hand is not a linear regression problem, but say a classification problem? To be more specific, assume that we're solving a logistic regression problem (or replace it by your favorite classification algorithm) with $$n$$ samples of dimension $$p$$. Now, irrespective of any distribution of the covariates/features, can we come up with a positive integer valued function $$f$$ so that $$p \ge f(n)$$ guarantees a perfect classification, i.e. zero training error (and not, small, positive training error)?

To be even more specific, let's consider the logistic regression, where given: $$\{(x_1,y_1) \dots (x_n,y_n)\} \subset \mathbb{R}^p \times \{0,1\},$$ one assumes: $$y_i|x_i \sim Ber(h_{\theta}(x_i)), h_{\theta}(x_i):= \sigma(\theta^{T}x_i), \sigma(z):= \frac{1}{1+e^{-z}},$$ and then finds the optimal parameter $$\theta*$$ of the model by: $$\theta^{*}:= arg \hspace{1mm}max_{\theta \in \mathbb{R}^p} \sum_{i=1}^{n}y_iln(h_{\theta}(x_i)) + (1-y_i)ln (1 - h_{\theta}(x_i))$$

Is there a guarantee, just like linear regression, that when $$p \ge f(n)$$ for a certain positive integer-valued function $$f,$$ the training error is always zero, i.e. $${\theta^{*}}^{T}x_i>0$$ when $$y_i =1$$ and $${\theta^{*}}^{T}x_i<0$$ when $$y_i =0,$$ irrespective of the distribution of $$x_i?$$ P.S. I understand that when $$p$$ is large enough, perhaps just $$p=n+1,$$ there exists $$\theta_1\in \mathbb{R}^p$$ so that $${\theta_1}^{T}x_i>0$$ when $$y_i =1$$ and $${\theta_1}^{T}x_i<0$$ when $$y_i =0,$$ but why does the same has to be true for $$\theta^{*}?$$

The same question for other types of regression problems? I know the my question is broad, so some links that goes over the mathematical details will be greatly appreciated!

Indeed, let us say that a point $$x_i$$ in your data is red if $$y_i=1$$ and blue of $$y_i=0$$. Let us say that $$\th\in\R^p$$ separates the red and blue points -- that is, has zero training error --- if $$\th^Tx_i>0$$ if $$x_i$$ is red and $$\th^Tx_i<0$$ if $$x_i$$ is blue.
Then, for any natural $$p$$, the zero training error cannot be attained by any $$\th$$ if e.g. (i) one of the $$x_i$$'s is $$0$$ or (ii) there are two red points of the form $$u$$ and $$au$$ for some real $$a\ge0$$ and some $$u\in\R^p$$ or (iii) there are two red points $$u$$ and $$v$$ and a blue point of the form $$au+bv$$ for some real $$a,b\ge0$$.
On the other hand, if the training data $$\{(x_1,y_1),\dots,(x_n,y_n)\}$$ admits some $$\th_*\in\R^p$$ that separates the red and blue points (that is, has zero training error), then your formula $$\th^*:= \text{arg max}_{\th\in\R^p}\sum_{i=1}^n(y_i\ln h_{\th}(x_i)+(1-y_i)\ln(1-h_{\th}(x_i))$$ makes no sense, because then the supremum of $$H(\th):=\sum_{i=1}^n(y_i\ln h_{\th}(x_i)+(1-y_i)\ln(1-h_{\th}(x_i))$$ over all $$\th\in\R^p$$ is not attained. Rather, this supremum (equal $$0$$) is "attained" only in the limit, when $$\th=t\th_*$$, $$t\to\infty$$, and, as above, $$\th_*\in\R^p$$ separates the red and blue points (that is, has zero training error).
• Thank you for your answer and observing that if $\theta^{*}\in \mathbb{R}^p$ does a perfect classification, then any positive multiple of $\theta^{*}$ does so as well and hence $H(\theta)$ doesn't have a maximum. I also well noted the three points you mentioned before when no $\theta$ can separate them. But for point (ii) did you mean that there's a red and a blue point of the form $u$ and $au, a\ge 0$ respectively (so that one of them will always get misclassified)? But I wonder about this: if we change the signs of $y_i$ from $\{0,1\}$ to something else, say $\{a,b\},$ (contd) Nov 20, 2020 at 22:18
• (contd) then the classification problem won't change, but I wonder if we can still apply some modified version of the counterexamples (i)-(iii) you gave that heavily depends upon the fact that $y_i=0$ or $1.$ Of course this is my mistake, as I should've given general $y_i$'s. In fact as long as $y_i's$ take two different values, that still doesn't change the problem. But thanks to your counterexample, I do see the trouble with setting $y_i=0$ or $1.$ Nov 20, 2020 at 22:23
• @Learningmath : I don't see any problem with how you label the $x_i$'s. Actually, and in my counterexamples I relabeled $1$ as red and $0$ as blue. However, in the expression for $H(\theta)$ we should keep the (standard) values $1$ and $0$ for the $y_i$'s, if we want to get meaningful results. Nov 22, 2020 at 0:37