Test Your Knowledge
Question 1 of 4
What is the core principle behind Random Forest that makes it more effective than a single decision tree?
It uses a completely different algorithm than decision trees
It combines multiple decision trees and aggregates their predictions
It only selects the single best decision tree from many candidates
It forces all trees to use the same features for splitting
Question 2 of 4
What is bagging (Bootstrap Aggregating) in the context of Random Forest?
A technique for compressing the forest model to save storage space
The process of removing weak trees from the forest
A method for training trees on random subsets of the data with replacement
The way Random Forest visualizes its decision boundaries
Question 3 of 4
Which of the following is a key hyperparameter in Random Forest that controls model complexity?
Learning rate
Maximum depth of trees
Activation function
Momentum
Question 4 of 4
How does Random Forest handle feature importance compared to a single decision tree?
Random Forest cannot determine feature importance at all
Random Forest provides more reliable feature importance by averaging across multiple trees
Random Forest randomly assigns importance to features
Random Forest only considers the most important feature from each tree