What are random forests and how do they work for regression tasks?

What are random forests and how do they work for regression tasks?

Random forests are an ensemble learning method that combines multiple decision trees to improve predictive accuracy. For regression, they aggregate the predictions of individual trees to produce a more accurate and stable output.

Can you explain how the model is trained?

Sure! The model is trained by creating multiple decision trees using random subsets of the training data and features. Each tree is built independently, and during prediction, the average of all tree predictions is taken.

What are the advantages of using random forests over other regression methods?

Random forests are robust to overfitting, handle large datasets well, and can capture complex relationships in the data. They also provide feature importance, which helps in understanding the influence of different variables.

Are there any limitations I should be aware of?

Yes, some limitations include longer training times with large datasets, less interpretability compared to single decision trees, and potential difficulty in handling very high-dimensional data.