What is bagging, and how is it related to Random Forest?
- Bagging involves combining predictions from multiple models, and Random Forest is an example
- Bagging involves using a single strong model
- Bagging is a type of boosting
- Bagging is unrelated to Random Forest
Bagging (Bootstrap Aggregating) is a method that involves combining predictions from multiple models, each trained on a random subset of the data. Random Forest is a specific example of a bagging algorithm that uses decision trees as the base models.
Loading...
Related Quiz
- Why might you choose to use Polynomial Regression in a model?
- ________ learning is often used for discovering hidden patterns in data.
- The ________ gate in an LSTM controls which parts of the cell state should be updated.
- Why is it problematic for a model to fit too closely to the training data?
- To avoid overfitting in large neural networks, one might employ a technique known as ________, which involves dropping out random neurons during training.