Error-Correcting Output Codes (ECOC) for Machine Learning

Error-Correcting Output Codes (ECOC) for Machine Learning

Machine learning algorithms, like logistic regression and support vector machines, are designed for two-class (binary) classification problems. In this tutorial, you will discover how to use error-correcting output codes for classification. As such, these algorithms must either be modified for multi-class (more than two) classification problems or not used at all. The Error-Correcting Output Codes method is a technique that allows a multi-class classification problem to be reframed as multiple binary classification problems, allowing the

Machine learning algorithms, like logistic regression and support vector machines, are designed for two-class (binary) classification problems.

As such, these algorithms must either be modified for multi-class (more than two) classification problems or not used at all. The Error-Correcting Output Codes method is a technique that allows a multi-class classification problem to be reframed as multiple binary classification problems, allowing the use of native binary classification models to be used directly.

Unlike one-vs-rest and one-vs-one methods that offer a similar solution by dividing a multi-class classification problem into a fixed number of binary classification problems, the error-correcting output codes technique allows each class to be encoded as an arbitrary number of binary classification problems. When an overdetermined representation is used, it allows the extra models to act as “error-correction” predictions that can result in better predictive performance.

In this tutorial, you will discover how to use error-correcting output codes for classification.

After completing this tutorial, you will know:

  • Error-correcting output codes is a technique for using binary classification models on multi-class classification prediction tasks.
  • How to fit, evaluate, and use error-correcting output codes classification models to make predictions.
  • How to tune and evaluate different values for the number of bits per class hyperparameter used by error-correcting output codes.

Let’s get started.

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