What is the principle behind the Random Forest algorithm?
- Ensemble of trees, increased complexity
- Ensemble of trees, reduced variance
- Single decision tree, increased bias
- Single decision tree, reduced bias
Random Forest is an ensemble learning method that operates by constructing multiple decision trees during training and outputs the mode of the classes for classification or the mean prediction of individual trees for regression. By combining many trees, it generally reduces overfitting and provides a more accurate prediction.
Loading...
Related Quiz
- How can interaction effects be included in a Multiple Linear Regression model?
- ElasticNet is a hybrid regularization technique that combines the L1 penalty of ________ and the L2 penalty of ________.
- In Simple Linear Regression, the method of _________ is often used to estimate the coefficients.
- In the context of K-Means clustering, what challenges may arise with poorly initialized centroids?
- What is the significance of the slope in Simple Linear Regression?