Why is the accuracy of a random forest typically higher than that for a single tree?

Why is the accuracy of a random forest typically higher than that for a single tree?

But the random forest chooses features randomly during the training process. Therefore, it does not depend highly on any specific set of features. Therefore, the random forest can generalize over the data in a better way. This randomized feature selection makes random forest much more accurate than a decision tree.

Is random forest bagging or Boosting?

Random Forest is one of the most popular and most powerful machine learning algorithms. It is a type of ensemble machine learning algorithm called Bootstrap Aggregation or bagging. The Bootstrap Aggregation algorithm for creating multiple different models from a single training dataset.

Why do we need bias in random forest?

Random Forest uses a modification of bagging to build de-correlated trees and then averages the output. As these trees are identically distributed, the bias of Random Forest is the same as that of any individual tree. Therefore we want trees in Random Forest to have low bias.

How are bagged trees special cases of random forest?

Note that bagged tree can be seen as a special case of random forest, where the number of predictors to consider for each split of a tree (i.e., mtry) equals the number of all available predictors.

How does the random forest reduce variance in predictions?

To sum up, the Random Forest employs a number of techniques to reduce variance in predictions while maintaining (to some extent) the low variance that was characteristic of the lone Decision Tree. It does this primarily by averaging together a number of very weakly correlated (if not completely uncorrelated) trees.

Why do all bagged trees have the same structure?

Since the boostrap samples used to train each individual tree come from the same data set, it is not surprising that the trees may share some similar structure. This similarity, known as tree correlation, is an essential factor that prevents further reduction of variance (hence further improvement of performance) for bagged trees.