Notation of expectation with conditional in subscript Inside the book "The Elements of statistical learning", I stumbled upon the following notation (Ex. 2.7)
$$E_{\mathcal{Y|X}}(f(x_0) - \hat{f}(x_0))^2$$
where  $\mathcal{X, Y}$ are two random variables representing the training data.
I would like to know how to formally define $E_{\mathcal{Y|X}}$. I know what a condition expectation is, and that some authors are using $E_{Y|X}[Y|X]$ to denote the condition expectation of $Y$ given $X$. However, we don't have a  condition expectation in this example, do we?
A second quite common notation is something like $E_X[Y]$. Here, we use the probability distribution of $\mathbb{P}_X$ to compute the expectation.
$$E_X[Y] = \int Y \mathbb{P}_X $$
However $\mathcal{X|Y}$ is not a random variable. So, I really don't know how the define $E_{\mathcal{Y|X}}$.
This question is related to an answer given by Dilip Sarwate
pipes vs commas in expectation notation subscript
Here, a definition of $E_{\mathcal{Y|X}}$ wasn't found.
P.S. Is there any guide for common notation used in ML. I have a rather heavy math background, and I'm often confused by the way how probability theory is applied.
 A: The calligraphic "$\mathcal{X}$" describes the collection of all the $x_i$-values in your dataset, and the calligraphic "$\mathcal{Y}$" all the $y_i$-values. So, while both $\mathcal{X} = (x_1,\ldots,x_N)^t$ and $\mathcal{Y} = (y_1,\ldots, y_N)^t$ are random vectors, the $\mathcal{X}$ are considered fixed and for the $\mathcal{Y}$ the conditional PDF $p(\mathcal Y | \mathcal X)$ is considered. And this conditional PDF is given by the equation in the exercise: $y_i = f(x_i) + \varepsilon_i$.
And since $\hat f(x_0)$ is a function of $\mathcal Y$:
$$
\hat f(x_0) = \sum_{i=1}^N \ell_i(x_0; \mathcal X)y_i,
$$
the expectation $E_{\mathcal{Y|X}}(f(x_0) - \hat{f}(x_0))^2$ is well defined.
Note, that $x_0$ is not necessarily an element of $\mathcal X$.
A: When you have a conditional expectation function, the input should be a function of the stipulated value of the conditioning variable.  The conditional expectation is non-trivial only if the input function also depends on the random variable whose behaviour is random.  In the present case, without having more context from the book, it is unclear how the argument $(f(x_0) - \hat{f}(x_0))^2$ depends on the two random variables at issue.
Taking a guess from my knowledge of regression models, I would say that $x_0$ is intended to be the stipulated conditioning value for $\mathcal{X}$ and $\hat{f}$ is probably intended to be a fitted regression function that depends implicitly on $\mathcal{Y}$.  If this supposition is correct then we can make the implicit dependence explicit, and the meaning of the conditional expectation (written as an integral using the conditional density) would then be:
$$E_{\mathcal{Y|X}}(f(x_0) - \hat{f}(x_0))^2
= \int \limits_\mathscr{Y} (f(x_0) - \hat{f}(x_0,y))^2 f_\mathcal{Y|\mathcal{X}}(y|x_0) \ dy,$$
where $\mathscr{Y}$ is the range of the random variable $\mathcal{Y}$.  You should also bear in mind that there is a difference between cases where we input a fixed conditioning value into a conditional expectation function (so that the answer is a constant) and cases where we input a random variable as a conditioning value into a conditional expectation function (so that the answer is a random variable).  You can find a related answer here that discusses this difference.
