Rule-based systems are a set of predefined rules that make decisions or provide recommendations by evaluating data against stored rules. Machine learning systems, on the other hand, use algorithms to make predictions or take actions without explicit programming. Both systems have their advantages and disadvantages, but hybrid systems that combine both rule-based and machine learning algorithms are becoming increasingly popular. These systems provide more robust, accurate, and efficient results, particularly when dealing with complex problems. Hybrid systems offer practical benefits such as fast implementation, robustness to outliers, and increased transparency. They are beneficial when combining business logic with machine learning capabilities, providing a more comprehensive and accurate solution to complex problems.

source update: How Can Hardcoded Rules Overperform ML? – Towards AI


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