How to Do Machine Learning: Six Steps for Every Problem
Introduction
Machine learning is hard. It's one of the most complex and challenging fields of computer science. But it's also one of the most rewarding because you can do amazing things when you get it right. Every problem is different, and there's no silver bullet that will always work. But a few building blocks don't change, no matter what problem you're trying to solve. In this blog post, I will share my process for doing machine learning: six steps that will help you tackle any problem!
1. It all starts with the data
The first step is understanding your data. This step might seem obvious, but it's incredible how often people try to build models without knowing what they're working with. Before you do anything else, take the time to explore your data. Look at the distributions of features, see if there are any patterns or correlations, and get a feel for what's going on. This step is essential for two reasons: first, understanding your data will help you choose the suitable model, and second, it will help you spot potential problems early on.
2. The evaluation of the solution
The evaluation criterion is what you will use to determine whether your model is good enough. This can be something like accuracy, precision, recall, or a custom metric specific to your problem.
"Good enough" will depend on the context of your problem. In some cases, an 80% accuracy may be good enough, while you may need 99% accuracy in others. It is essential to have a clear understanding of what is acceptable for your problem before moving on. Otherwise, you may spend too much time optimizing your model and never reach an adequate level of performance.
It is also important to note that we should choose the evaluation criterion before starting the optimization process. This way, you can avoid the temptation of tweaking your model until it performs well on your evaluation metric, even if it is not improving your model's performance on the task you care about.
3. Come up with a simple Baseline Model
What's the simplest model you can come up with to serve as a baseline for your problem? This baseline is important because you need to have some benchmark to compare your more sophisticated models against. A simple baseline could be randomly returning predictions or a hard-coded heuristic (if-then-else). Sometimes it's shocking how hard it is to beat these simple baselines.
4. Put the basics together for an end-to-end solution
Once you have your baseline, it's time to start iterating on more sophisticated models. Try different algorithms, tune hyperparameters, and so on. The goal here is to gradually improve the performance of your models until you reach a point where you are satisfied with the results.
5./6. Choose, Train and evaluate a Model
You can use many different algorithms for training, so it's essential to experiment and find the one that works best for your data. Once you've trained a model, it's time to evaluate it. Now you compare the performance of your model against the baseline.
If the performance is good, then you're done! If not, it's back to the drawing board to try something else. But don't get discouraged. You'll eventually find a model that works well for your problem with a little trial and error.
Conclusion
So, that's how I got to a model that makes decent predictions. There's still a lot of work to be done — most of it in the monitoring and maintenance phase — but it's nice to have something working in the background. Hopefully, this will help you get started on your machine learning journey. If you have any questions or want more details about anything I've covered here, please don't hesitate to reach out!
