The ABC of Machine Learning Models
A Mental Framework for Developers
In a world where machine learning is increasingly shaping how decisions are made it’s easy to get caught up in the complexity and flashiness of the latest models. But beneath the layers of neural nets, hyperparameter tuning, and cutting-edge benchmarks lies a quieter truth: simplicity, context, and grounded thinking matter more than ever.
To help cut through some of the noise, I want to offer a framework that keeps things grounded. It’s simple, memorable, and useful. In other words, it’s as simple as A-B-C.
A-B-C: Accuracy, Baseline, Complexity.
Each letter reflects a mental checkpoint to help you assess whether your model is actually solving the right problem in the right way.
A is for Accuracy (But with Context)
Accuracy is often the first metric data scientists reach for and for good reason. It’s intuitive, quantitative, and measurable. However, if you just take accuracy for its face value, accuracy can be misleading.
Are you measuring accuracy globally, or across key subgroups?
Are false positives and false negatives equally costly?
Does a higher accuracy actually translate to better decisions in the real world?
A classifier that’s 95% accurate in a highly imbalanced dataset may be less effective than one with a lower accuracy but better precision and recall for the minority class.
The biggest lesson here is:
Don’t stop at accuracy. Translate it into impact.
B is for Baseline (Know What You’re Beating)
I’ve been guilty of forgetting this and I’m sure a lot can relate to the rush to build and deploy. On cases like this, it’s easy to forget to ask a simple question: what’s the baseline?
What would happen if we always predicted the most frequent class?
Could a basic rule-based system get us 80% of the way?
How does our model compare to the status quo?
Baselines aren’t just for academic papers. They’re essential in production, too. If your model only marginally outperforms a heuristic or worse, if it doesn’t outperform at all, you may be overengineering for minimal gain.
A good baseline keeps your ambition honest and grounded.
C is for Complexity (Simplify to Scale)
Complexity is seductive. More features, deeper networks, fancy architectures, these features lure us with the promise of better performance and technical prestige. But in reality, when designing an overcomplex model can you answer the following questions:
Can someone else maintain the model six months from now?
Can you explain its decisions to a non-technical stakeholder or customer?
Is the performance gain worth the interpretability loss?
In ML, complexity isn’t always badge of honor, it’s a cost. Every additional moving part introduces new risks: harder debugging, longer training times, or brittle pipelines.
The core takeaway is:
Start with the simplest model that works. Only upgrade when needed.
Putting It Together
The ABC of machine learning isn’t just a checklist, it’s a mindset. It encourages practitioners to:
A - Validate metrics and accuracy in the right context
B - Benchmark against grounded alternatives
C - Build systems that are sustainable in the long run, not just sophisticated
In a field moving as fast as ML, these principles help you stay anchored, because the real magic in AI isn’t in the model. It’s in the thinking of the human behind it.
But I want to hear about your experience, what mental models do you use to produce ML models? Share yours in the comments!
As always…
Thanks for reading! ✌️
this is a simple and straightforward roadmap for ML. accuracy, baseline and complexity. I like how more data scientists are now emphasizing simplicity for scalable models.