What are random forests and how do they work for classification?

What are random forests and how do they work for classification?

Random forests are an ensemble learning method that combines multiple decision trees to improve classification accuracy.

How do they handle overfitting compared to a single decision tree?

They reduce overfitting by averaging the results of multiple trees, which helps to generalize better on unseen data.

Can you explain the importance of feature selection in random forests?

Feature selection is crucial as it helps in reducing noise and improving the model's performance by focusing on the most relevant features.

What are some common applications of random forests?

Common applications include medical diagnosis, fraud detection, and customer segmentation.