“MLshorts” 52: Overfitting ⚠️ in Machine Learning

How to spot it before it’s too late

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Overfitting? What is that

When I first started building Machine Learning models, I thought getting 99% accuracy on training data meant I had built something cool and amazing.

You look the metrics and feel proud, until you tested it on new data and you see performance decreasing.

Great! you discovered overfitting.

In Machine Learning, overfitting happens when a model learns the training data too well, including the noise, random things and tiny details that do not actually represent the real world. Instead of learning general patterns, the model starts memorizing examples.

A simple way to think about it is like studying for an exam by memorizing answers instead of understanding concepts. If the exact same questions appear, you score perfectly. But the moment the questions change slightly, you struggle.

This is one of the most common traps in ML, especially when you first start building models.

The tricky part is that overfitting often looks like success at first and your training accuracy keeps improving and your loss keeps decreasing. Your model seems to get “better and better”, but in reality, it is becoming worse at generalizing.

One of the biggest warning signs is a huge gap between training performance and validation performance. For example, imagine your training accuracy climbs to 98%, while validation accuracy stays stuck at 75%. First red flag.

Another common sign is when training loss keeps decreasing endlessly, but validation loss stops improving or even starts increasing. Your model is still learning, but it is learning the wrong thing. It is fitting noise instead of signal.

And perhaps the most painful sign of all: your model performs terribly on unseen data, despite looking excellent during development.

In real world projects it is very tempting to keep adding more layers, more trees, or more complexity because training performance keeps improving. But:

more complexity does not always mean better learning.

So how do you fix overfitting?

The first thing is often simplifying the model. Smaller neural networks, fewer trees, or limiting tree depth can help the model focus on the important patterns instead of memorizing details.

Regularization techniques are also extremely useful. Methods like L1/L2 regularization or dropout essentially “penalize” the model for becoming too complex.

Another powerful solution is simply having more data. The more diverse examples your model sees, the harder it becomes to memorize specific cases.

And if you are training neural networks, early stopping can save you from a lot of pain. Instead of training endlessly, you stop training once validation performance stops improving.

One lesson I learned the hard way is this:

Never trust training accuracy alone.

Always monitor both training and validation metrics together. If training keeps improving while validation stagnates, your model is probably memorizing noise instead of understanding patterns.

And you know what? Whenever your model performance looks “too good to be true”, it probably is! 😁

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