Optimizing Random Forest

HunGrl
HunGrl New Altair Community Member
edited November 2024 in Community Q&A
Hello! :) I'm working on a random forest predictive model that predicts a binary label, in my case whether a customer has paid in advance or not. I have the following attributes:
date, article code, product name, producer, unit price , sales quantity, customer id, county, payment habits.

The process involves data reading, missing value is not in the data set, normalization (Z transform) (unit price, quantity), cross-checking the training data.

Performance is not good: accuracy about 75%, recall weighted 51%, precision weighted 58%.

I'm not sure whether what I am doing is right or wrong.

How can performance be improved? Any suggestions?
Sorry for my bad english
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