What are the best hyperparameters for Random Forest?
The best hyperparameters for Random Forest often include the number of trees (n_estimators), maximum depth (max_depth), and minimum samples split (min_samples_split).
How do I choose the number of trees?
A common approach is to start with 100 trees and then use cross-validation to see if increasing the number improves performance.
What about max_depth? Does it really matter?
Yes, max_depth controls how deep each tree can grow. Limiting it can prevent overfitting, especially with noisy data.
Can you suggest some values for min_samples_split?
Typically, values like 2, 5, or 10 are used. It depends on your dataset size; larger datasets may benefit from higher values.
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