0

I am working with a data set where the response variable is binary and 15-20 continuous and categorical variables.

I am using the naiveBayes library to compute the model. I am interested in understanding which of the independent variables are the most important as well as trying to better understand the model outputs (below as example).

enter image description here

I've done a lot of research but I am not having any luck. Any perspective would be greatly appreciated.

Michael

Michael Lamontagne
  • 11
  • Can you explain why you chose Naive Bayes, esp. since you have continuous predictors? NB is not the most natural fit for continuous features (as hinted in a way by Age[,1] and Age[,2]). NB also makes very strong assumptions. See here and here. – dipetkov Apr 05 '22 at 06:45
  • Fair point. The majority of the variables are categorical with only a few that are continuous.

    I am testing out various methods to predict and better understand what variables have a stronger impact/influence on a response variable that is binary. Based on my research, it looks like naive bayes might be a good option.

     – Michael Lamontagne Apr 05 '22 at 11:27

0 Answers0