A weak learner is an algorithm that performs only slightly better than random guessing on a given problem, i.e. slightly better than flipping a coin. It typically has low complexity and tends to underfit the data. However, when combined with other weak learners through techniques such as boosting, it can be used to build a strong learner that makes highly accurate predictions.
A strong learner, on the other hand, is an algorithm that can learn complex patterns in the data and make highly accurate predictions with very low error rates. It typically has high complexity and can overfit the data if not properly regularized.
Difference between a strong learner and a weak learner
The following table depicts clearly the main differences between a weak learner and a strong learner.
|Weak Learner||Strong Learner|
|- An algorithm that performs only slightly better than random guessing|
- Error rate is slightly below 50%
- Low complexity, tends to underfit data
- Multiple weak learners can be combined (using boosting) to form a strong learner
- Examples: Decision Stump, Logistic Regression with few features
|- An algorithm that can learn complex patterns in the data and makes highly accurate predictions|
- Error Rate is low
- High complexity, can overfit data if not regularized properly
- Examples: Gradient Boosting, Deep Neural Networks, SVM, Bagging
Therefore, the difference between a weak learner and a strong learner lies in their ability to learn from data and make accurate predictions. A weak learner is a simple algorithm that can only make slightly better than random guesses, while a strong learner is a complex algorithm that can learn complex patterns and make highly accurate predictions.
Illustrative example: Comparing performance of Gradient Boosting and Random Forest (Strong Learners) with a Single tree (Weak Learner) for a Cancer study
The following graph taken from the book ‘An Introduction to Statistical Learning’ is an illustrative example of performance of Strong Learners such as Boosting and Random Forest compared to a Weak Learner (Single tree) in a classification problem. As mentioned in the footnote, the test error rate for a single tree, i.e. a Weak Learner is 24%. As several weak learners are combined through Boosting and Random Forest to form a strong learner, the test classification error is significantly reduced to about 15% for Random Forest and 10% for Boosting algorithms after 1000 iterations.
Note: This example is for illustration purposes only. The performance of different models can vary based on data and modeling process