Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
runArgs = {
"iterations" : 2000, # NULL defaults to (max available - 1)
"trials" : 5, # 5 recommended for the dummy dataset
}
My understanding on trials is that, while performing multiple trials, each trial takes different variables (not all the variables, it considers few out of it) & samples. and it picks up the best trial of it. Don't we lose information because of taking few variables (not the complete list of input variables)?
whatever variables we use in MMM all are important right. Please explain if my understanding is correct.
I rather think each trial takes all the variables but has different (random) starting points for each hyperparameters, and iterations within each trial will optimize these parameters
Hi,
runArgs = { "iterations" : 2000, # NULL defaults to (max available - 1) "trials" : 5, # 5 recommended for the dummy dataset }
My understanding on trials is that, while performing multiple trials, each trial takes different variables (not all the variables, it considers few out of it) & samples. and it picks up the best trial of it. Don't we lose information because of taking few variables (not the complete list of input variables)? whatever variables we use in MMM all are important right. Please explain if my understanding is correct.