Making a Good Algorithm Choice in Machine Learning Projects
One of the most difficult pieces of the puzzle when starting a machine learning project is figuring out what type of machine learning algorithm you will need to use. The answer might not be obvious at the start, and there are several factors that you need to consider before making a final decision. Choosing the wrong machine learning algorithm can have a devastating effect on your ability to get useful results from your project. It can also bog you down, taking up your time without delivering useful results.
Everyone in your organization should be on the same page when it comes to choosing the machine learning algorithm that the project will use. It should be a careful decision that takes up a lot of time and resources before you finally decide to move on to the actual project. There are several important factors, but some are more important than others. For example, your training data will often be the most important factor when making a final decision.
Training data is typically the most useful measure of whether you will have a successful machine learning project or not. In supervised learning, training data is a critical part of the process. The quality of your training data will typically make or break the overall project. If you don’t have enough training data, you might have to generate it using the various tools out there.
You also need to think about your training data in the context of future management and development. This then allows you to make the correct choice in terms of what algorithm you will use for your project. Unsupervised learning is a difficult case, which needs its own specific set of things you need to consider before taking the plunge. Either way, effective management of your training data will impact which algorithm you can use.
How Accurate Do You Need to Be?
What would the output look like? Machine learning is about making predictions based on data. However, not every prediction needs to be accurate to the 10th decimal point. General accuracy might be good enough, and that is the other consideration when trying to choose a good machine learning algorithm. You also want to understand how each variable changes how the results can be interpreted, meaning that the algorithm chosen will have an impact. This is where you have to make various trade-offs that come with choosing the right algorithm based on accuracy and other factors.
The factors above make up a significant portion of the problem when choosing machine learning algorithms. However, you can also look at whether you need to train quickly for your machine learning project. Training a machine learning algorithm requires time and plenty of computing power. This computing power is not cheap and can typically make up a significant portion of your budget. That means you will have to focus intensively on optimizing things to train as quickly as possible. You also want to look at the number of features required, which can make your project go longer. The final set of considerations involves unsupervised learning and supervised learning. Each method has its own set of algorithms for you to use, and they make a big difference in the overall procedure.