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Classifiers are perhaps the oldest form of AI in existence. The original perceptron demonstration in 1957 (the first neural network) was itself a binary classifier. Nearly 70 years later, classification remains one of the most important decision models in existence, and underpins the operations of nearly all AI systems in some capacity.

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Why humans love classifiers

For one thing, they’re simple to understand. Humans easily process discrete decision units, and it’s easy to model a decision problem as a classification problem. Should I walk or drive to work? Should I buy a red, blue, or yellow shirt? Should I hire this person or not? Another reason: the math is simple. Accuracy is easy to understand - (8/10 correct). Precision/recall and f1 scores are a bit more complex, but foundational for data scientists. Lastly, they’re flexible. The various types of classifiers extend the model to cover nearly any type of real-world decision. We’ll cover a few of the canonical ones in this guide.

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Why humans hate classifiers

Classification is often the first time someone has to swallow a hard truth about machine learning: the fact that classifiers not always correct is a feature, not a bug. If you’re making some kind of decision or prediction that can be 100% correct by design, that means you should be building some kind of deterministic filter, not a predictive classifier. If there is no deterministic filter you can use, yet you found yourself with a 100% correct classifiers on some sample data - you can reasonably expect it to be overfit to that data and not generalize well to data out of sample. The idea that a system not working 100% of the time is where many software engineers making a jump to AI engineering often have to make a mental leap. So the goal has to shift: instead of looking for 100% reliability, can you reset expectations to producing a model that is roughly a good as, or better than a human expert at that same task?