So let's say I have data with numerical variables A
, B
and C
.
I believe that the value of A
has an effect on B
.
I also believe that A
and B
both have an effect on C
.
I don't think C
has an effect on either A
or B
.
I want to use machine learning to predict A
, B
, and C
. I obviously have A
and B
as training data, and I have other variables as training data too.
Do I simply create multiple models to predict all three, or is there a way to make one model predict them all if I just throw the entire dataset at it?
A
,B
, andC
. I obviously haveA
andB
as training data." I don't understand this statement. Why do you want to predictA
andB
if you already have this information? I don't understand what do you want to predict, and which variables are you going to use to predict, ie: which are your features and which is your target? $\endgroup$