The differences between ML and Rule-Based systems is explained with the example of a spam filter. The traditional systems are based on a set of characteristics that identify an email as spam, which have some drawbacks because the spam emails keep changing over time and the system must be upgraded with these adjustments, and this process is untractable due to code maintenance and other issues. The ML systems can be trained with features and targets extracted from the rules of Rule-Based systems, a model is obtained, and it can be used to predict new emails as spam or normal emails. The predictions are probabilities, and to make a decision it is necessary to define a threshold to classify emails as spam or normal ones.
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