The ultimate goal of every machine learning or artificial intelligence project is to build a successful model that can be put into production. It is the most important part of the data science lifecycle. However, how do you know whether your model works or not during each phase of the project? This is where model validation and model evaluation come into play. Model validation is essentially ensuring that your model works as it should during each step of the lifecycle. You can think of it as the model evaluation part of the process.
As with any project, you want to ensure that your results are good before feeding those results into another project. As the saying goes, garbage in, garbage out. We do model validation to ensure that garbage in garbage out does not apply to our project. It is a core part of developing exceptional machine learning models.
Model validation can be defined as the processes you use to ensure that your model performs as it should. The fact that there are multiple processes means that it isn’t just one step. You do model validation because you want to ensure that you are not wasting time and money working on something that will fail in production.
In the data science lifecycle, many things can go wrong, but this is typically one of the worst problems you can face. You also have data governance requirements and risk management opportunities to think about. Many people often mistake model evaluation and validation. While these words sound similar, they could not be more different. You have to always be on your toes to ensure you are not making a bad decision when it comes to this process.
Compared to Evaluation
How does it compare to model evaluation? The main difference is that model evaluation is typically done when training. It is all about selecting an algorithm and determining which one performs the best. Validation is done on fresh data sets. You do it after testing has been completed, but you also do it before everything is deployed. You can think of it as running a series of checks to ensure that your work will be useful in whatever you are trying to achieve.
The worst thing that can happen is to spend time and energy developing models that flop in production. That could cause a major crisis for companies, which is why it is a major problem that is often avoided. Companies try to do as much as possible to ensure that models are evaluated and validated before anything that can go wrong.
Making Sense of It All
Validating a model involves three different areas. These areas are the input, calculation, and output. Each phase deals with a part of the data science lifecycle. For example, you use back testing as a way of dealing with the input phase.
Ultimately, the main thing you need to remember is that model validation ensures you don’t deploy flawed models. The overwhelming majority of machine learning initiatives fail, and this is one of the easiest ways for you to join that crowd.