I'm working on a model to understand the predictors (variables) importance, and whether going forward I should include them in the model which should scale over > 1M rows. Here is a sample of my data:
type | div | predicted med standard win med express loss high new loss low standard win
I've been using R (but I'm open to other tools such as Python), in which I created a model using just "type" for now, and used this command:
library(caret) modelFit <- train(predicted~., data = train_data, method="rf") varImp(modelFit)
The output is:
Overall divstandard 100 typelow 100 typemed 50
However, I'm looking for what of the variables (either "div" or "type") are more relevant to predicting a "win" or "loss". Ideas on how to find the most important variable in terms of prediction?
I've also looked into using excel with this, in particular the CORREL function, but that doesn't seem to work well with non-numeric variables.