If you’ve ever wanted to really understand random forest regression, this one is a solid walkthrough.
The article builds a random forest regressor in C# from the ground up, showing how an ensemble of decision trees can reduce overfitting and improve predictions on unseen data. It also breaks down the practical knobs that shape the model, like tree depth, row and column sampling, and how bagging fits into the picture.
Good read if you like seeing the mechanics, not just the API.
Full article here: Random Forest Regression Using C# - Visual Studio Magazine
Do you prefer building ML concepts from scratch first, or jumping straight to the library tools?