Why are ‘What If’ Scenarios Essential to ML Model Processes?

Creating a machine learning, or ML model, takes a lot of testing. You are creating a system that learns off of data, but like any educational system, if the data input into the system is wrong, you’re going to get a lot of mistakes. This can mean a lot of wrong results in the ML model. There are a lot of ways you can comb through the system to troubleshoot issues that arise, but by far the most effective is the simple “What if” scenario: introducing an idea to your system that it will have to work deciphering and give a correct result to. Why is the “what if” scenario so effective? We explain in this guide.

What is machine learning?

ML is the shorthand for machine learning, which is the ability for a computer system to make decisions and carry out actions without the need for direct instructions. They do this by taking data from a preprogramed set of instructions and data set by the developer.

Machine learning has gained popularity lately thanks to the explosion of AI due to OpenAI launching its latest edition, ChatGPT4, but it has been around for a long time in some form of a more primitive state. You tell a calculator an algebra equation and it will give you an answer based on the mathematical theory.

‘What if” scenarios are basically the mathematical theory in this instance. “What if this piece of data is introduced? Does that change the formula, thereby giving a different result?” In order to create a machine learning model that is fully functional, giving the results you want and performing the action that you want, it’s important to go through various ‘what if’ scenarios in order to test the system. This will create a responsible AI system. What is responsible AI? Find out here.

It should account for every scenario

If you have a fully-rounded collection of scenarios to give the system, you will have covered every eventuality. No matter how unexpected or unlikely, with you feeding the ML model “what if” scenarios, you will have a machine learning algorithm that is ready for any event.

It will flag up issues that need fixed

As you feed your system “what if” scenarios, you will soon see that there are a lot of eventualities that it is not prepared for. For a fully-rounded ML model that can handle any situation, you will need to give it a lot of relevant hypotheticals to deal with. If it cannot give a correct result, you can look back through its processes to understand where and why it went wrong.

Additionally, a lot of these wrong results might be due to a lack of data. With “what if” scenarios showing the wrong results, you can feed it more data or any missing data that might impede a result.

It will keep your system updated

Feeding the algorithm “what if” scenarios shouldn’t end once the system is deployed for its purpose. In order to keep your system maintained and functional, you will have to keep feeding it “what if” scenarios as they arise or are considered. This will keep your system up to date as technology evolves and its particular function evolves. For example if you’re creating an app, maybe users will point out a function it can serve as it gains popularity.

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