How does a Random Forest algorithm reduce variance compared to a single decision tree?
- By increasing the depth of each tree
- By reducing the number of features used in each tree
- By training multiple trees and averaging their predictions
- By using a more complex set of decision rules
A Random Forest reduces variance by aggregating predictions from multiple decision trees. Each tree is trained on a different subset of the data, and their predictions are averaged, leading to a more robust and less overfit model compared to a single decision tree.
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