What Is Overfitting in Machine Learning?Overfitting in ML Explained Simply with Real Examples | M005

Author(s): Mehul Ligade

Originally published on Towards AI.

What Is Overfitting in Machine Learning?Overfitting in ML Explained Simply with Real Examples | M005

📍 Abstract

If you have ever trained a machine learning model that gave you a perfect-looking score — and then fell apart the moment it saw real-world data — you have already encountered overfitting. It is one of the most common and dangerous mistakes in the machine learning world. And worse, it is invisible unless you are looking for it.

Overfitting is tricky. It does not crash your program. It does not throw errors. It looks like success on the surface — until you try using your model for something real. That’s when it fails silently, and sometimes, spectacularly.

In this article, I want to help you understand overfitting the way I wish someone had taught me when I started. Not just the textbook definition, but what it feels like, how it sneaks into your models, how I’ve personally been misled by it in real projects, and what you can do to avoid it. This is not another recycled blog post with a chart and a two-line definition. This is the real story of overfitting, explained the way it deserves to be — clearly, deeply, and with examples that stay with you.

Let’s begin.

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1. Overfitting Looks Like Success — Until It Isn’t

In the early days of my machine learning journey, I used to chase accuracy like it was everything. I would spend hours tuning hyperparameters, adding more features, trying different models, all with one goal: make the score go up. The moment I hit 97%, 98%, even 99% accuracy, I’d celebrate. I thought I had built something smart.

But the celebration never lasted long.

Because as soon as I put the model on real-world data — data it hadn’t seen before — things would break. My beautiful predictions turned into noise. Confidence scores dropped. Business teams came back confused. And I was left wondering how something that looked perfect during training could fail so badly during testing.

That was my introduction to overfitting. A model that performs beautifully on the data it’s trained on, but performs terribly everywhere else. A model that memorizes answers instead of understanding patterns. And once I understood what was really happening, I started seeing overfitting everywhere →not just in code, but in thinking.

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2. What Overfitting Really Means

Most definitions will tell you that overfitting is what happens when a model “learns too much.” But that’s not quite right. Learning too much isn’t the problem. Learning the wrong things is. Overfitting is when your model stops learning the general rule and starts memorizing the quirks. It stops understanding and starts mimicking. And when it does, it becomes fragile.

Let me put it another way. In machine learning, your model is supposed to learn the pattern — the underlying relationship between input and output. But if the model becomes too flexible, too eager to reduce error, and too tuned to the training data, it starts picking up noise. It learns things that are technically correct for the dataset it saw, but completely useless anywhere else.

And here’s the worst part: the better your model performs on training data, the harder it becomes to believe that it’s wrong. Overfitting looks like success. It feels like progress. But it’s actually a trap.

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3. The Student Analogy That Changed Everything for Me

Here’s how I finally understood overfitting in a way that stuck with me forever.

Imagine two students preparing for the same math exam. One of them spends their time solving all kinds of problems, understanding the core concepts, and practicing with different question formats. The other student takes last year’s question paper, memorizes every answer line-by-line, and never actually understands what’s going on.

Now imagine both students walk into a new test. The questions are similar, but not identical. They test the same ideas, but with new numbers and wording.

The first student, the one who understood the concepts, may not remember every answer perfectly — but they’ll adapt. They’ll solve the problem by applying the same logic in a new way.

The second student? They panic. Because none of the questions match what they memorized. They weren’t prepared to think. They were prepared to recall.

That’s exactly how overfit models behave.

They are like students who aced the training exam but never learned the subject. The moment something changes; a small detail, a new pattern, a shift in distribution — they fall apart. They do not fail because they are bad. They fail because they never learned to generalize.

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4. My First Encounter With Overfitting (And How It Fooled Me)

I will never forget the first time I built a machine learning model that truly overfit. I was working on a regression task — predicting healthcare insurance costs based on demographic and lifestyle features. The dataset was clean. The features looked solid. I had built what I thought was a great model using gradient boosting.

When I checked the R² score on the training set, it was 0.99. Almost perfect. It was like the model had psychic powers. I had never seen anything perform this well. But the excitement did not last long.

The moment I ran the model on the validation set, the score dropped to 0.67. On the test set, it fell further. The predictions were inconsistent. I was stunned.

I checked the code three times. No errors. No typos. The model had simply overfitted. It had learned the tiny bumps and wrinkles in the training set even the anomalies and treated them as truth. It had memorized too well. And that’s when I realized: high training accuracy can be a red flag, not a green one.

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5. The Real Reasons Overfitting Happens

Overfitting is not a bug. It’s the natural consequence of giving a smart model too much freedom and not enough discipline. The more complex your model — the more layers in a neural network, the more branches in a tree ensemble, the more interactions in your features — the easier it becomes to perfectly match the training data. And the smaller your dataset, the faster it happens.

But here’s what nobody tells you. You can overfit even with simple models. If your features are poorly chosen, if you accidentally leak information, or if your evaluation method is flawed, overfitting can sneak in without you even realizing it.

It does not always come from doing something “wrong.” Sometimes it comes from doing everything right — but on data that is too specific, too narrow, or too noisy.

Overfitting is not about bad code. It is about shallow thinking.

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6. The Danger of Overfitting in the Real World

In school projects or competitions, overfitting is annoying. But in the real world, it is dangerous.

Imagine a fraud detection model that overfits to old transaction patterns and misses a new attack vector. Imagine a medical diagnosis model that learns from outdated or skewed patient data and misclassifies rare diseases. Imagine a recommendation system that memorizes short-term clicks but fails to understand long-term user intent.

These aren’t just modeling issues. They’re product risks. Business risks. Sometimes even life-or-death risks.

That’s why overfitting is not just a technical problem. It’s a mindset problem. It teaches you to stop chasing training metrics and start thinking like a systems builder. Like someone who cares about what happens after deployment — not just before.

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7. How to Detect Overfitting (Before It Wastes Your Time)

One of the hardest things about overfitting is that it doesn’t show up as an error. The model doesn’t crash. The training logs look amazing. The metrics keep improving — but only on the data the model has already seen. That’s why overfitting hides in plain sight. Unless you are watching closely, you’ll miss the signs until it’s too late.

The most reliable way to detect overfitting is by using a proper validation strategy. You need to keep a part of your data untouched — never shown to the model during training — and use that as your checkpoint. If your model does well on training data but performs poorly on validation data, you’re likely overfitting. And the wider that gap gets, the worse the overfitting is.

In one of my projects, I trained a classification model that reached nearly perfect precision and recall. But when I ran it on a validation set from a different time window, the performance dropped by more than 25 percent. That’s when I learned to never trust a model based on training results alone. A model that learns a pattern will perform well even on data it has not seen before. A model that memorizes will crumble the moment that pattern changes.

The second sign of overfitting is when your performance curve starts diverging. During training, both the training error and validation error should ideally decrease together. But if your training error keeps dropping while your validation error starts rising — that’s your red flag. It means the model is continuing to learn, but it’s learning things that are specific to the training data and harmful everywhere else.

The third sign is more subtle, but powerful: your model starts giving overly confident predictions that don’t match the complexity of the real-world problem. If it starts assigning 99% probabilities to answers that are actually uncertain, it may have picked up signals that are statistically meaningless but highly tuned to the training data.

These signs don’t require magic tools to spot. Just discipline. Train with transparency. Track your metrics. Compare behaviors across datasets. Look for patterns that hold, not just scores that shine.

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8. How to Prevent Overfitting > Not With Luck, But With Intention

Overfitting doesn’t happen because your model is bad. It happens because you let it learn too much without supervision. The only way to stop it is to train with intentional constraints.

One of the most powerful ways to prevent overfitting is to keep your model simpler than you think it needs to be. I used to believe that complex models were smarter. But I’ve seen simple linear models outperform deep trees when the data is clean and well-structured. Complexity is not a badge of honor. It’s a responsibility. The more complex your model, the more careful you need to be.

Another key method is regularization. Think of regularization as a speed bump for learning. It adds a penalty for overconfidence. In regression, for example, L1 and L2 regularization discourage the model from assigning extreme weight to any one feature. In neural networks, techniques like dropout randomly turn off neurons during training to prevent co-dependency.

Cross-validation is another underrated tool. It’s not just about splitting your data into folds. It’s about building a model that’s consistent, not just accurate. If your model performs well across different subsets of your data not just one lucky split, it’s a good sign that you’ve avoided overfitting.

And here’s something most people overlook: data quality. If your dataset is small, noisy, or biased, your model will latch onto the noise and treat it as a signal. Clean data leads to cleaner learning. And in many cases, adding more diverse examples not just more data is the fastest way to improve generalization.

You can’t stop overfitting with luck. You stop it by designing your workflow to reward generalization. That means evaluating your model the way real users will experience it — not just on the numbers that make you feel good during training.

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9. What Overfitting Taught Me About Real-World Machine Learning

The most important lesson I learned from overfitting is this: models are easy to build. But reliable models ‘models that work outside your notebook, that survive messy data’, that adapt when the world changes — are rare. And that reliability comes from how you think, not just how you code.

Overfitting taught me to slow down. To test my assumptions. To treat every model like a hypothesis something that needs to be challenged, not just optimized.

It also taught me humility. Because the more powerful your model is, the easier it is to fool yourself. A neural net with 50 layers can learn anything — even patterns that don’t matter. If you don’t control it, it will overfit faster than you can debug.

One time, I built a model for early disease prediction. The training accuracy was near perfect. But the deployment feedback was sobering. The model had latched onto age — a proxy for many outcomes — and used it as a shortcut. It had technically “learned,” but what it had learned was not useful for real diagnosis. That was a humbling moment. It reminded me no matter how advanced is ever smarter than the data you feed it. It can’t reason. It can’t generalize magically. It doesn’t “understand” anything. What it can do is find patterns in data and optimize according to what you define as success.

But if the patterns it sees are full of noise, or your data only reflects narrow situations, or your evaluation metric rewards the wrong behavior, then what it learns won’t be knowledge. It’ll be imitation. And that imitation will fail the moment the data shifts even slightly. That’s how overfitting silently breaks production systems: not by throwing errors, but by being confidently wrong at the exact moment it matters.

The solution isn’t to build smarter models. It’s to become a smarter ML engineer.

You become smarter by asking better questions. Not just “What’s the accuracy?” but “Is this accuracy meaningful?” Not just “Does the model learn fast?” but “Is it learning the right thing?” You become smarter by testing your models on more than one slice of data. By checking how they behave under edge cases. By designing your workflow to stress-test the very assumptions you’re making. And you become smarter by building with humility > the kind that knows even the best algorithm is still just a tool.

I now treat every model I build like a first draft, not a final solution. No matter how good the numbers look, I assume it’s wrong somewhere. That assumption alone has saved me from deploying fragile models more times than I can count. Because what matters is not whether your model works on the data it saw. What matters is whether it can handle the data it hasn’t seen yet — the data that will show up tomorrow, next month, next year.

Overfitting teaches you to think like a scientist, not just a software developer. It teaches you to be skeptical, to demand evidence, to respect generalization over gimmicks. And once you learn to recognize it — not just as a technical issue, but as a design principle — you begin to build models that are truly useful. Models that don’t just score high, but survive in the wild.

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11. What Comes Next

We’ve just tackled one of the most critical ideas in Machine Learning: overfitting. If you made it this far, you now understand more than most beginners ever will.

You now know that a model that scores 99% accuracy in training might actually be broken. You know that a model’s job is not to remember but to generalize. And you know that building robust ML systems requires more than code — it requires critical thinking, careful validation, and real curiosity about how your model behaves outside your IDE.

In the next article, I’ll be writing about L1 and L2 regularization — not just how they work, but why they matter so much in real projects. I’ll explain how they shape your model’s learning behavior, how they reduce overfitting by design, and how I use them when working with both tabular data and neural networks.

And like everything I write, it won’t be theory-heavy or recycled. It will be drawn from my actual work — the bugs I’ve debugged, the weird behaviors I’ve chased down, and the principles I’ve learned to trust after building, failing, rebuilding, and finally succeeding.

If you want to learn ML in a way that actually prepares you for the real world — not just exams, but messy, unpredictable, deployment-ready systems — then follow along.

📍 Connect with me:

X (Twitter): x.com/MehulLigade
LinkedIn: linkedin.com/in/mehulcode12

I share everything I learn — with the clarity I wish I had when I started. Let’s build models that last.

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Published via Towards AI