Weak Learner Vs. Strong Learner

(1) Introduction:

In machine learning, the terms “strong learner” and “weak learner” refer to the performance and complexity of predictive models within an ensemble or learning algorithm.
These terms are often used in the context of boosting algorithms.

A weak learner is a model that performs slightly better than random guessing or has limited predictive power on its own.
Weak learners are typically simple and have low complexity, such as decision stumps (a decision tree with only one split), shallow decision trees, or linear models with low-dimensional features.
Despite their limited individual performance, weak learners can still contribute to the ensemble’s overall performance when combined appropriately.
Weak Classifier: Formally, a classifier that achieves slightly better than 50 per cent accuracy.
The most commonly used type of weak learning model is the decision tree. This is because the weakness of the tree can be controlled by the depth of the tree during construction.
The weakest decision tree consists of a single node that makes a decision on one input variable and outputs a binary prediction, for a binary classification task. This is generally referred to as a “decision stump.”

k-Nearest Neighbors, with k=1 operating on one or a subset of input variables.
Multi-Layer Perceptron, with a single node operating on one or a subset of input variables.
Naive Bayes, operating on a single input variable.

Week learners are the individual models to predict the target outcome. But these models are not the optimal models. In other words, we can say they are not generalized to predict accurately for all the target classes and for all the expected cases.

They will focus on predicting accurately only for a few cases. If you see the above example.
The original dataset has two possible outcomes:
- Red
- Green

The above representation predicts the target red or green with some features.
The first learner accurately predicted the green, the second weak learner also accurately predicting the green. Whereas the last weak learner is accurately predicting red. As we said before, weak learning accurately predicts one target class.
Combining all the weak learners makes the strong model which generalized and optimized well enough for accurately predicting all the target classes.

A strong learner is a model that achieves high predictive accuracy or low error rates on its own.
Strong learners are often complex and have a higher capacity to capture complex relationships or patterns in the data.
Examples of strong learners include deep neural networks, ensemble methods like Random Forests or Gradient Boosting Machines (GBM), or models with a large number of features and complex decision boundaries.
We seek strong classifiers for predictive modeling problems. It is the goal of the modeling project to develop a strong classifier that makes mostly correct predictions with high confidence.

We said a combination of all the weak learners builds a strong model. And I think the above figure explains this well.
How do these individuals build trains at once, how do they perform the predictions?
Based on the way the individual models (weak learners) training phase the bagging and boosting methods will vary.

The concept of weak and strong learners is closely tied to boosting algorithms, such as AdaBoost and Gradient Boosting, where weak learners are sequentially combined to create a strong ensemble model. The boosting algorithm focuses on improving the performance of weak learners by assigning higher weights to misclassified instances, thus allowing subsequent weak learners to concentrate on these difficult cases.

The strength of the ensemble comes from the collective ability of weak learners to complement each other’s weaknesses and make up for individual shortcomings. By combining weak learners, the ensemble can achieve better performance than any of the individual models alone.

It’s worth noting that the definitions of weak and strong learners can vary depending on the context and the specific algorithm being used. In some cases, a weak learner may refer to a model with limited complexity, while in others, it may refer to a model with limited accuracy. Similarly, the definition of a strong learner may differ based on the specific requirements and goals of the task at hand.