Using Optimization and Machine Learning to Get More Accurate Insights
Machine learning is now a mainstream technology used by massive companies like Facebook, Netflix, and Google. However, businesses need to know that they can still use optimization techniques to get even more results from machine learning. As machine learning continues to become more popular, businesses will need to figure out additional ways of improving their results. Optimization is a must-use technique, and it will provide complementary features that many machine learning engineers can use to be more accurate. Machine learning and optimization approach problem-solving in different ways, but the result is better accuracy for the problem you are trying to solve. It is crucial to understand how to make both of these technologies better, as it will help provide great insights for your business.
What Is Optimization?
Machine learning works by using data to make predictions in the future. Therefore, your machine learning results will only be as good as the data you feed your models with. If your models are fed with bad data, you won’t be able to get accurate insights. It is why data generation and model preparation is such an important part of machine learning. Optimization takes a different approach. Optimization deals with using constraints to come up with the best action to take. It uses mathematical methods to solve problems regarding the constraints placed on it. For example, you know you need to deliver 20 packages along the route, but you don’t know the right combination of trucks and timing to do it. You can feed these constraints into an optimization program, and it can help you find the most optimal method to use. For example, it could tell you to use five trucks from 6 AM to 6 PM to deliver these packages. Optimization works when you have knowledge about the business and how it needs to function. It is essentially providing answers to a knowledgeable practitioner, which is what makes it different from machine learning. Machine learning cannot provide any useful insights without any data.
How Machine Learning and Optimization Relate
While you need to understand how the business works to perform optimization algorithms, machine learning can learn about the business from the data you feed into it. It actually trains itself to come up with accurate answers based on this training data. Using this understanding, you can come up with a way of making both of these methods work for your business needs. For example, if you have no data, you can start by using optimization to find accurate ways of reaching your business goals. An example of this would be using optimization to see which marketing channels will give you the best results. However, you might not always know what the optimal constraints are for your optimization programming. You can use machine learning to help you find the most optimal outcome and generate the constraints that your optimization program will solve.
Differences and Similarities
As mentioned above, the main differences between these two ways of decision-making have to do with data. Machine learning makes great decisions with a lot of data, and optimization requires no data, but you need to have a great understanding of the problem you are trying to solve. When you combine these two methods of solving problems, you get more accurate decisions to help you reach your business objectives.