I have a dataset with 15 input columns, and 4 output column names. These 4 outputs are related to each other.
I have fitted four different GLM models to predict them.
But I want neural networks/random forests/other machine learning techniques.
What would be the best approach for this problem if I want to have:
- A single model with multiple outputs (Continuous values)
- Preferably gives me the distribution of the output rather than a single value.
- Takes into account the correlation in the output space. These four values are somewhat related to each other. X1>X2>X3>X4 always.
Any articles/guides etc would be greatly appreciated.