What are some common errors in Random Forests?

What are some common errors in Random Forests?

Common errors in Random Forests include overfitting, underfitting, and issues with feature importance.

How can I avoid overfitting in my Random Forest model?

To avoid overfitting, you can limit the maximum depth of the trees or increase the minimum samples required to split a node.

What does feature importance mean in this context?

Feature importance indicates how much each feature contributes to the model's predictions, helping to identify key variables.

Are there any specific metrics to evaluate a Random Forest model?

Yes, you can use metrics like accuracy, precision, recall, and the F1 score to evaluate your Random Forest model's performance.