Multi-Output Classification using scikit Decision Trees
Regarding Problem 1, if you expect the different components of the target value to be independent, you can approach the problem as building a classifier for every component. That is, if the features are F = (F_1, F_2, ..., F_N) and the targets Y = (Y_1, Y_2, ..., Y_N), create a classifier with features F and target Y_1, a second classifier with features F and target Y_2, etc.
Regarding Problem 2, if you are not dealing with a time series, IMO the best you can do is simply predict the most frequent value for each feature.
That said, I believe your question fits better another stack exchange like cross-validated.
Disclaimer: I'm new to the field of Machine Learning, and even though I have done my fair share of research during the past month I still lack deep understanding on this topic.
I have been playing around with the scikit library with the objective of learning how to predict new data based on historic information, and classify existing information.
I'm trying to solve 2 different problems which may be correlated:
Problem 1
Given a data set containing rows R1 ... RN with features F1 ... FN, and a target per each group of rows, determine in which group does row R(N+1) belongs to.
Now, the target value is not singular, it's a set of values; The best solution I have been able to come up with is to represent those sets of values as a concatenation, this creates an artificial class and allows me to represent multiple values using only one attribute. Is there a better approach to this?
What I'm expecting is to be able to pass totally new set of rows, and being told which are the target values per each of them.
Problem 2
Given a data set containing rows R1 ... RN with features F1 ... FN, predict the values of R(N+1) based on the frequency of the features.
A few considerations here:

Most of the features are categorical in nature.
Some of the features are dates, so when doing the prediction the date should be in the future relative to the historic data.
The frequency analysis needs to be done per row, because certain sets of values may be invalid.

My question here is: Is there any process/ML algorithm, which given historic data would be able to predict a new set of values based on just the frequency of the parameters?
If you have any suggestions, please let me know.