Using individual predictions from binary classification model to predict class proportions in group Suppose I have a group of users of a paying app and I want to predict each month the users that are not going to renew their subscription. This is called churn rate. To do that I create a binary classification model that, based on individual features of each user like usage time, device used, etc..  will calculate the probability of renewing/not renewing.
Would it be a valid approach to use this model on each user of my app and calculate this proportion based on those individual predictions?. Or Would it be better to build a model that tries to predict said churn rate (%) for the month, instead of aggregating the individual predictions as in the first approach (similar to a time series prediction problem)?
My understanding is that using the individual predictions is not going to have the same effect. First I have to choose a probability threshold to classify an instance in a positive or negative instance. But the criteria to choose that threshold does not have to be aligned with predicting the right proportion of positive classes

You do not have to choose a threshold, and I am unsure why you think you have to. (Obligatory link to my fundamental doubts about thresholding.)
Suppose you have $n$ users, each with a predicted probability of $p_i$ of churning. Then the total number of users you expect to churn is simply $p_1+\dots+p_n$, and the total expected churn proportion is $\frac{p_1+\dots+p_n}{n}$.
Yes, of course you could also model and predict either one of these numbers separately (possibly accounting for the fact that the numbers will not be below $n$ or above $n$, respectively $1$). Either this "top-down", or a "bottom-up" approach may yield better predictions. Or you might use the "optimal reconciliation" approach to hierarchical forecasting to combine both predictions, which I usually find to yield best predictions across the board.
