How do I tune hyperparameters for a Random Forest model?

How do I tune hyperparameters for a Random Forest model?

Tuning hyperparameters for a Random Forest involves adjusting parameters like n_estimators, max_depth, and min_samples_split. Would you like to know how to optimize these?

Yes, what are the best practices for optimizing n_estimators?

A good practice is to start with a range of values, like 100 to 1000, and use cross-validation to evaluate performance. You can also use Grid Search or Random Search for systematic tuning.

What about max_depth? How should I approach that?

For max_depth, you can experiment with values from 5 to 30. A deeper tree can capture more information but may lead to overfitting, so monitor the validation score.

Can you explain min_samples_split as well?

Sure! min_samples_split determines the minimum number of samples required to split an internal node. A common starting point is 2, but increasing it can help prevent overfitting.