📌 Multi-Objective Optimisation in ML Summary
Multi-objective optimisation in machine learning refers to solving problems that require balancing two or more goals at the same time. For example, a model may need to be both accurate and fast, or it may need to minimise cost while maximising quality. Instead of focusing on just one target, this approach finds solutions that offer the best possible trade-offs between several competing objectives.
🙋🏻♂️ Explain Multi-Objective Optimisation in ML Simply
Imagine you are packing a bag for a trip and want to fit in as many clothes as possible, but also keep the bag light. You cannot make the bag both very light and very full, so you need to find a balance. Multi-objective optimisation in machine learning is like finding the best way to pack your bag while considering all your goals at once.
📅 How Can it be used?
Use multi-objective optimisation to design a recommendation system that balances accuracy and fairness for different user groups.
🗺️ Real World Examples
In autonomous vehicle design, engineers use multi-objective optimisation to create navigation algorithms that balance safety, speed, and energy efficiency. The system must make driving decisions that do not only minimise travel time but also reduce the risk of accidents and conserve battery power, ensuring the vehicle operates safely and efficiently in various conditions.
When developing machine learning models for healthcare diagnostics, researchers often need to optimise for both accuracy and interpretability. A highly accurate model may be too complex for doctors to understand, while a simpler model might not be as precise. Multi-objective optimisation helps to find models that offer a good balance between these two important factors.
✅ FAQ
Why would you need to balance more than one goal in machine learning?
Many real-world problems are not about achieving just one thing. For example, you might want a machine learning model that is not only accurate but also runs quickly on a mobile phone. Sometimes, you need to keep costs low while still getting good results. Multi-objective optimisation helps you find the best mix, rather than sacrificing one important aspect for another.
How is multi-objective optimisation different from just improving one thing in a model?
When you focus on a single goal, like accuracy, you can sometimes end up with a model that is too slow or too expensive to use. Multi-objective optimisation looks at several goals at once, so you can find solutions that work well across the board. It is about finding the best trade-offs and not just chasing one perfect score.
What are some examples of using multi-objective optimisation in machine learning?
One example is designing a recommendation system that needs to be both relevant and respectful of user privacy. Another is creating a medical diagnosis tool that should be highly accurate but also fast enough for use in emergencies. In both cases, you cannot focus on just one goal, so multi-objective optimisation helps you balance what matters most.
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