What are random forest ensemble methods?

What are random forest ensemble methods?

Random forest ensemble methods are machine learning techniques that use multiple decision trees to improve prediction accuracy.

How do they work?

They work by creating a 'forest' of decision trees, where each tree is trained on a random subset of the data, and their predictions are averaged for final output.

What are the advantages of using random forests?

Advantages include high accuracy, robustness to overfitting, and the ability to handle large datasets with higher dimensionality.

Can you give me an example of where they are used?

Sure! Random forests are commonly used in fields like finance for credit scoring, in healthcare for disease prediction, and in marketing for customer segmentation.