Strong Learners vs. Weak Learners in Ensemble Learning
It is common to describe ensemble learning techniques in terms of weak and strong learners.
For example, we may desire to construct a strong learner from the predictions of many weak learners. In fact, this is the explicit goal of the boosting class of ensemble learning algorithms.
Although we may describe models as weak or strong generally, the terms have a specific formal definition and are used as the basis for an important finding from the field of computational learning theory.
In this tutorial, you will discover weak and strong learners and their relationship with ensemble learning.
After completing this tutorial, you will know:
- Weak learners are models that perform slightly better than random guessing.
- Strong learners are models