Optimizing Random Forest
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:
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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Hi @HunGrl
Please watch these videos both may give some ideas. I will also recommend taking the Machine Learning professional certification is completely free and will help you better understand all these topics.
https://academy.rapidminer.com/learn/enroll/6f1a15c5-093f-4468-88fb-c16d984cff6f
Sampling & Weighting demo | RapidMiner Studio
Optimize demo | RapidMiner Studio