Random forests – making trees more reliable
Decision trees are not only useful for their transparency and interpretability. They are also fundamental building blocks for more powerful ensemble models that combine many individual trees, while randomly varying their design to address the overfitting problems we just discussed.
Why ensemble models perform better
Ensemble learning involves combining several machine learning models into a single new model that aims to make better predictions than any individual model. More specifically, an ensemble integrates the predictions of several base estimators, trained using one or more learning algorithms, to reduce the generalization error that these models produce on their own.
For ensemble learning to achieve this goal, the individual models must be:
