How can I optimize a random forest model for better accuracy?

How can I optimize a random forest model for better accuracy?

To optimize a random forest model, you can tune hyperparameters like the number of trees, max depth, and min samples split.

What are the best hyperparameters to start with?

A good starting point is to set the number of trees to 100, max depth to None, and min samples split to 2.

How do I evaluate the performance of my model after optimization?

You can use metrics like accuracy, precision, recall, and F1-score, along with cross-validation for a robust evaluation.

Are there any specific libraries in Python for this?

Yes, popular libraries include Scikit-learn for model building and Hyperopt or Optuna for hyperparameter optimization.