I am looking for resources (books, lecture notes, etc.) about techniques that can handle data that have multiple-targets (Ex: three dependent variable: 2 discrete and 1 continuous).
Does anyone have any resources/knowledge on this? I know that it is possible to use neural networks for this.
Random forest handle it rather well, see Would a Random Forest with multiple outputs be possible/practical? or scikit learn’s documentation. I guess GBM or any tree based method can be adapted in a similar fashion.
More generally, when you run any learning algorithm minimizing a score, you usually work on minimizing ∑i(pi−yi)2 which is one-dimensional. But you can specify any target function. If you were working on (two-dimensional) position prediction, ∑i(ˆyi−yi)2+(ˆxi−xi)2 would be a good metric.
If you have mixed type output (classification and regression) then specifying the target function will probably require you to specify a target function that gives more weight to some targets than other: which scaling do you apply to continuous responses ? Which loss do you apply to miss-classifications?
As for further academic reading,
The Landmark Selection Method for Multiple Output Prediction
(deals with high dimensional dependent variables)