What is the difference between Decision Trees, Bagging and Random Forest?
A decision tree serves as the building block of most bagging and boosting algorithms and is always built using the concept of maximizing information.
A decision tree serves as the building block of most bagging and boosting algorithms and is always built using the concept of maximizing information.
Bagging, or “Bootstrap Aggregation”, refers to an ensemble design structure in which each instance of the ensemble, such as an individual decision tree in a Random Forest, is created on a different subset of the original dataset.
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