Ensembling and Random Forest Part 2 | Strong Learners vs Weak learner | Rohit Ghosh | GreyAtom
Channel: GreyAtom EduTech
Date: 19/07/2019

Ensembling and Random Forest Description

Ensemble learning, in general, means making a prediction by combining the predictions of different individual models. In this way, the ensemble model tends to be more flexible (less bias) and less data-sensitive (less variance).

Random forest is an ensemble model using bagging as the ensemble method and decision tree as the individual model.

Strong learners vs Weak learner.

A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing). ... When first introduced, the hypothesis boosting problem simply referred to the process of turning a weak learner into a strong learner.


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