Random Forest is an ensemble method that consists of a multitude of decision trees and uses a technique known as __________ to create diversity among them.
- Bagging
- Boosting
- Bootstrapping
- nan
Random Forest uses bagging (bootstrap aggregating) to create diversity among its constituent decision trees by training each tree on a different random subset of the data.
Loading...
Related Quiz
- You have a dataset with numerous features, and you suspect that many of them are correlated. Using which technique can you both reduce the dimensionality and tackle multicollinearity?
- The ________ component in PCA explains the highest amount of variance within the data.
- When visualizing clusters in high-dimensional data...
- In a case where a company wants to detect abnormal patterns in vast amounts of transaction data, which type of neural network model would be particularly beneficial in identifying these anomalies based on data reconstructions?
- What is the Adjusted R-Squared, and how does it differ from the R-Squared?