I am a beginner learning machine learning.
I built a Random Forest classifier to predict student performance using a dataset from Kaggle. My model currently achieves about 87% accuracy.
I would like to know what are some common ways to improve the performance of a Random Forest model. Should I focus on feature selection, parameter tuning, or data preprocessing?
Any suggestions would be appreciated.