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How to Do Machine Learning Perceptron Classification Using C#

Dr. James McCaffrey of Microsoft Research uses code samples and screen shots to explain perceptron classification, a machine learning technique that can be used for predicting if a person is male or female based on numeric predictors such as age, height, weight, and so on. It's mostly useful to provide a baseline result for comparison with more powerful ML techniques such as logistic regression and k-nearest neighbors.

• By James McCaffrey
• 01/07/2020

·         Perceptron classification is arguably the most rudimentary machine learning (ML) technique. The perceptron technique can be used for binary classification, for example predicting if a person is male or female based on numeric predictors such as age, height, weight, and so on. From a practical point of view, perceptron classification is useful mostly to provide a baseline result for comparison with more powerful ML techniques such as logistic regression and k-nearest neighbors.

·         From a conceptual point of view, understanding how perceptron classification works is often considered fundamental knowledge for ML engineers, is interesting historically, and contains important techniques used by logistic regression and neural network classification. In fact, the simplest type of neural network is often called a multi-layer perceptron.

·         Additionally, understanding exactly how perceptron classification works by coding a system from scratch allows you to understand the system's strengths and weaknesses in case you encounter the technique in an ML code library. For example, the Azure ML.NET library has a perceptron classifier, but the library documentation doesn't fully explain how the technique works or when to use it.