Machine Learning Algorithms Explained – Random Forests

Machine Learning Algorithms Explained – Random Forests

4 Random Forests are supervised ensemble-learning models used for classification and regression. Ensemble learning models aggregate multiple machine learning models, allowing for overall better performance. The logic behind this is that each of the models used is weak when employed on its own, but strong when put together in an ensemble. In the case of Random Forests, a large number of Decision Trees, acting as the “weak” factors, are used and their outputs are aggregated, with the result representing the “strong” ensemble. [Read More]