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I want to compare different error rates of different classifiers with the error rate from a weak learner (better than random guessing). So, my question is, what are a few choices for a simple, easy to process weak learner? Or, do I understand the concept incorrectly, and is a weak learner simply any benchmark that I choose (for example, a linear regression)?

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A weak learner is a classifier/predictor, which performs tasks poorly. Its accuracy can be said as average, but not satisfactory. Our main task in machine learning is to improve the performance of the weak classifier.

You can use the following method to solve this problem:

  • Optimization algorithms like Gradient descent, AdaBoost, Stochastic gradient descent and many more.

  • Data preprocessing, e.g. better feature selection and feature extraction, which can help to train model accurately.

  •  And many more.

The performance of weak learner is quite similar to random guessing. Using some boosting algorithm can boost the accuracy of the final ensemble. 

A classifier or predictor with a slow prediction rate is also called a weak learner.

Until the algorithm supports weighted data instances, any algorithm can be used for boosting.

Hope this answer helps.

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