The Twin Pitfalls of Machine Learning: Avoid Overfitting and Underfitting
Learn how to avoid overfitting and underfitting in machine learning. Master key techniques to build models that generalize and perform well on real-world data.
What is machine learning?
It’s all about teaching computers to learn from data and make smart choices on their own. But here’s the thing—not every model gets it right.
Sometimes, they go overboard trying to remember everything. Other times, they don’t learn enough to be helpful at all.
When you’re trying to build a model that actually works well, you’ll often run into two common problems. One is when the model gets way too focused on the training data.
The other is when it just doesn’t pick up on the patterns at all. Both can mess things up, just in different ways.
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In this article, we’ll look at what causes these problems, how to tell when they’re happening, and what you can do to fix them.
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The Twin Pitfalls of Machine Learning
Just think about getting ready for a test. If you were to memorize every single practice question and do great on those but then freeze up when the real test throws different questions at you—that’s overfitting.
Basically it’s when the model is too smart for its own good. We do not want this, but then there is the opposite too.
Now flip it. Say you only skim the material and don’t really learn much. You go into the test without a solid grip on anything, and you don’t do well. That’s underfitting.
In machine learning, it’s kind of the same idea. Overfitting is when your model gets too focused on the training data—almost like it memorized it. So yeah, it does great on that data, but when it sees something new, it falls apart.
Underfitting is the opposite. It’s when your model is too simple and misses the patterns completely, even in the data it was supposed to learn from.
Why Overfitting even happens
There are some general or I should say common reasons for this. Overfitting usually shows up for a few main reasons:
The model’s too complex – If you’re using something like a deep neural network on a tiny dataset, it’s like using a rocket to drive down the street. It’s overkill, and it ends up memorizing instead of learning.
Not enough data – When you don’t have much data to train on, the model just learns every little detail by heart, instead of picking up on real patterns.
Too many features – If your data has a ton of columns or variables, it becomes easy for the model to latch onto random noise and treat it like it matters.
No regularization – If you don’t have anything in place to keep the model in check, it’ll go wild trying to fit everything perfectly—even the stuff it shouldn’t..
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How to Fix Overfitting
The good news? There are plenty of ways to deal with overfitting. These are just a few but I want you to see that there is always a solution to any problem we encounter, it’s up to us how to handle it based on our data and model.
Make the model simpler – Try using fewer features or go with a more basic algorithm that doesn’t overthink things.
Add more data – The more examples you give your model, the better it gets at spotting real patterns instead of just memorizing.
Use cross-validation – This helps test your model on different chunks of data, so you can see how it performs on stuff it hasn’t seen before.
Regularization – This basically tells the model not to go too far when trying to fit the data by adding a little penalty when it overdoes it.
Tune the settings – Adjust things like learning rate, tree depth, or number of layers to find the sweet spot where your model works best without overfitting.
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The Importance of Model Generalization
Let’s say you’re teaching a kid to spot cats. If you only show them pictures of your fluffy white cat, Muffin, they might start thinking every cat looks just like her. Then you show them a black cat or a hairless one, and suddenly they’re confused.
That’s a sign they didn’t really learn the idea of “cat”—they just memorized Muffin.
In machine learning, generalization is about how well your model can handle new data it hasn’t seen before. That’s the whole goal. If your model only works on the data you trained it with and falls apart when it sees something new, it’s not doing its job.
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Regularization: Keeping Models in Check
I don’t want you to confuse generalization with regularization because they are not quite the same thing, they’re still different.
Regularization is kind of like giving your model a little self-control. Instead of letting it chase every twist and turn in the data, regularization helps it stay steady and not get too carried away. In plain terms, it adds a small penalty if the model gets too complicated. That way, it doesn’t overdo it.
There are two main types we’ll look at: Ridge and Lasso.
If you’d like to read more about the ways to prevent overfitting and gain insights into Hyperparameter tuning with a code breakdown the check out the full article ⤵️
Stop Overfitting in Machine Learning: 5 Proven Fixes That Work Fast
Machine learning is really just about spotting patterns in data and using those patterns to make smart guesses or decisions.
Conclusion
At the end of the day, machine learning isn’t just about feeding a bunch of data into a model and crossing your fingers. It’s about finding the right balance—where the model picks up on real patterns without overcomplicating things or missing the point.
Overfitting and underfitting are just bumps in the road. What matters is being able to recognize when they show up, figure out what’s causing them, and knowing how to fix them.
With the right approach and a bit of trial and error, you can build models that actually get it—not just memorize the answers.
Hope you all have an amazing week nerds ~ Josh (Chief Nerd Officer 🤓)
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