I want to make supervised learning on a dataset containing for each observation a list of labels (the predictors) and a list of types to predict. The train dataset looks like this:
label1 label2 label3 type1 type2 1 book novel <NA> person writer 2 fly tree eggs animal bird 3 state <NA> <NA> country <NA> 4 music band piano album <NA>
I know how to apply machine learning when there is only one variable Y to predict, but I was wondering how to do when there are multiple variables Yi. In my case I would basically want to predict a list of types from a list of labels (knowing that the number of labels and types may vary as shown in the example).
In a more practical way, I was wondering if I should transform the types into binary variables (there might be more than 100 types) like this:
label1 label2 label3 person writer animal bird country album 1 book novel <NA> 1 1 0 0 0 0 2 fly tree eggs 0 0 1 1 0 0 3 state <NA> <NA> 0 0 0 0 1 0 4 music band piano 0 0 0 0 0 1
Is multivariate analysis the field I should investigate? I am a newbie in ML so my question may be naive though... Thanks for any help!