What Is Overfitting in Machine Learning? Causes and Fixes

What is overfitting in machine learning?

Overfitting is what happens when a machine learning model memorizes its training data instead of learning the general pattern, so it performs well on examples it has seen and poorly on new ones. The model looks excellent in testing. Then it disappoints in real use.

In practice, this is one of the most common problems in AI projects. Many people ask what is overfitting only after a launch goes wrong. Everything looks fine during training, so the trouble only shows up after launch. In this guide, we explain the idea in plain language and walk through the causes, the warning signs, and the fixes.

The Google Machine Learning Crash Course page on overfitting describes it the same way. A model fits the training set so closely that it cannot make correct predictions on new data. You can read the source pages through the links in this article.

What is overfitting? A simple exam analogy

Picture a student preparing for an exam. First, the student memorizes the answers to every past question, word for word. Then, if the same questions appear, the student gets a perfect score. However, when a question changes slightly, the student has no idea what to do, because the answers were memorized and the subject never was.

An overfitted model behaves the same way. Because of that, it treats random details, noise, and one-off exceptions in the training data as if they were real rules. So the shortest answer to the question "what is overfitting" is a model that memorizes.

A student who truly understands the subject works differently. Then, when a new question arrives, that student applies the underlying principles. A good model does the same thing: it extracts the general rule from training data and carries it over to examples it has never seen. Put simply, we call this ability generalization.

Why does a model start to memorize?

While a model trains, its only goal is to reduce the error on the training data. If the model has high capacity, meaning many adjustable parts, the easiest way to reach that goal is to memorize examples one by one. Instead of searching for the general rule, it stores a custom answer for each example.

As training goes on, the model first captures the large, general patterns. Then it tries to shrink the remaining small error too. Finally, at that stage it starts to fit the noise. Training error keeps falling, but performance on new data gets worse.

In short, memorization does not come from bad intent. Instead, it comes from the nature of optimization. If you do not add safeguards, the model will try to reproduce the training data as perfectly as it can. Therefore, preventing overfitting is part of designing the training process itself.

The business parallel makes it clear what is overfitting in everyday work. For example, if you train an employee only on the answers to past reports, that person struggles with a new report. Expecting something different from a model would be unrealistic.

What do training error and validation error tell you?

First, training error shows the loss on the data the model learned from. Second, validation error shows the loss on a separate slice of data that the model never saw during training. When you watch both side by side, you can tell whether the model is truly learning.

In practice, healthy training means the two curves fall together. In other words, as the model picks up new patterns, both errors shrink. When overfitting begins, the curves separate: training error keeps dropping, while validation error flattens and then starts to rise.

The Google Machine Learning Crash Course explains this split through loss curves. However, if the curves drift apart, the model is probably memorizing the training set. So we recommend plotting both curves in every training run.

Also, read the curves by their overall trend, not by a single point. Validation error can wobble for a short time, and one wobble is not an alarm. Still, if it climbs for several rounds in a row while training error keeps falling, you have real overfitting. That diverging pair of lines is the visual answer to the question of what overfitting is.

• Both errors low: the model generalizes, which is a healthy sign.
• Training error low but validation error high: a classic sign of overfitting.
• Both errors high: a sign of underfitting.

What is overfitting, and how do you spot it in a business?

In practice, you can notice some signs even without a technical team. The clearest sign is a model that looked great in testing but disappoints in live use. On paper, the accuracy in the report is high, yet real decisions do not meet expectations.

Second, look for a model that reacts too strongly to small changes in the data. If predictions swing wildly after you add a few new examples, the model may be clinging to noise. Finally, a quick drop in performance over time is the third sign.

1. Ask for training and validation results separately.
2. Check whether the gap between the two is large.
3. Test the model on completely fresh data it has never seen.
4. Monitor live performance at regular intervals.

If the gap between training results and new-data results is large, the question of what is overfitting applies to your project too. In that case, review the data and the model complexity first.

What is underfitting?

In short, underfitting means the model fails to learn enough from the data. It is so simple, or so briefly trained, that it does badly on both the training data and new data. It is the opposite of overfitting, so understanding what is overfitting also means understanding underfitting.

Back to the exam analogy: a student who never studied can solve neither the old questions nor the new ones. So the error is high everywhere. Because even the training error stays high, this problem is usually easier to notice.

Typical causes include a model that is simpler than the task requires, training that ends too early, important input information that never reaches the model, and regularization that is too strong. In practice, the fix is often to give the model more capacity, more meaningful inputs, and longer training.

Be careful, though. While you fix underfitting, it is easy to slide into overfitting. Instead of enlarging the model all at once, increase its capacity step by step and check validation results at each step. That way, you do not miss the balanced zone between the two extremes.

What is generalization, and why is it the real goal?

Generalization is a model's ability to predict correctly on new examples it did not see during training. In other words, what makes a model valuable is not its score on training data, but this ability. In business, a model always decides about customers, products, or periods it has never met before.

The Google Machine Learning Crash Course lists a few assumptions behind good generalization. First, examples should be drawn independently, the data should not change over time, and the training, validation, and test sets should come from similar distributions. If those assumptions break, even a good model struggles in practice.

For example, a model trained on last month's data may work worse after customer behavior shifts. That is not classic overfitting, but the symptom looks similar. Therefore, you should measure performance regularly, not just once.

Why split data into training, validation, and test sets?

To measure overfitting, you need data the model has not seen. Therefore, you usually split your data into three parts. First, the training set is what the model learns from. Then you use the validation set to try out settings during development. Finally, the test set stays untouched as a single, unbiased exam.

The Google Machine Learning Crash Course warns about using the test set too often. Also, the more you experiment against the same test set, the more the model adapts to it. This creates a "studying for the test" effect, and the measurement loses its reliability.

• Training set: the examples the model learns its parameters from.
• Validation set: the examples you use for tuning and model selection.
• Test set: the examples you look at once, before the final decision.

However, split ratios vary by project. In addition, the test set must not contain copies of training examples. Duplicates make the result look better than it is, and they mislead you.

What causes overfitting?

According to Google's material, overfitting has two main roots. The first is training data that does not represent the real world. The second is a model that is more complex than the problem needs. In practice, these two causes often work together.

• Too little data: the model cannot extract a general rule, so it memorizes examples.
• Noisy data: wrong labels and random errors look like patterns.
• An overly complex model: too many parameters make memorizing easy.
• Training for too long: the model eventually fits even the noise.
• Unrepresentative examples: the training data does not reflect real users.
• Data leakage: test information slips into training without anyone noticing.

However, each cause has a different remedy. For example, if data is scarce, you collect more or expand it. If the model is too complex, you simplify or constrain it. Finally, if training runs too long, you use early stopping. So choosing a cure before you find the root cause only wastes time.

How does data augmentation reduce overfitting?

Data augmentation creates meaningful new variations from the examples you already have. So when you show the model more and more varied examples, memorizing gets harder and learning the general rule gets easier. In image projects, slightly rotating, cropping, or brightening a photo is a typical case.

Also, a similar idea applies to text and tabular data, but you need more care. If a generated example represents a situation that cannot happen in real life, it hurts the model. So make sure the label stays valid after the change.

However, augmentation is not a miracle by itself. The real solution is collecting real, representative data. Still, when collection is expensive, augmentation is a good helper. For the basics of preparing data, see our guide on how to use AI for data analysis.

How does regularization prevent overfitting?

Regularization is a technique that penalizes model complexity during training. The model no longer tries only to reduce error. At the same time, it pays a price for very large weights. As a result, it leans toward simpler solutions, and the chance of memorizing drops.

The Google Machine Learning Crash Course presents L2 regularization as the most popular example. It pulls weights toward zero but never pushes any of them exactly to zero. L1 regularization, on the other hand, can drive some weights all the way to zero, so it removes unnecessary inputs.

A single coefficient sets the strength of regularization. If it is high, the model is constrained too much and underfitting becomes more likely. On the other hand, if it is low, the constraint is weak and overfitting remains a risk. You tune this balance by watching validation results.

Deep learning also uses similar techniques, such as dropout. If you are curious about the foundations of neural networks, our article on what is deep learning is a good start.

When does early stopping help?

Early stopping means ending training when validation error starts to rise. Even if training error is still falling, the moment validation error changes direction is the moment the model starts memorizing. So stopping at that point prevents overfitting before it begins.

The appeal of the method is its simplicity. Also, you do not design an extra penalty term. You simply watch the validation curve. The Google Machine Learning Crash Course calls this approach simple, but it also notes that it is rarely as good as regularization that constrains complexity directly.

In practice, it makes sense to combine both. Regularization keeps the model simple from the start, and early stopping catches any memorization that still slips through. Because validation error can fluctuate, define a patient threshold instead of stopping at the first uptick.

Why does cross-validation give a more reliable measurement?

Cross-validation splits the data into several parts and uses a different part for validation in each round. You test the model against a new part every time, then average the results. As a result, this reduces the effect of one lucky or unlucky split.

Above all, the method is especially valuable when data is scarce. For instance, a small validation set can fluctuate randomly and mislead you. Several rounds show you both the average performance and how consistent the results are.

One warning: for time ordered data, such as monthly sales, do not shuffle at random. If information from the future leaks into the past, the results look better than they should. Instead, train on earlier periods and validate on later ones.

Cross-validation increases computing cost, yet for a trustworthy decision it is usually worth it.

How do you tune model complexity?

Model complexity decides how flexible a pattern the model can learn. A model that is too simple underfits, and a model that is too complex overfits. The goal is the middle point that gives as much flexibility as your problem actually needs.

In practice, the usual workflow is to start simple and grow in a controlled way. First, you build a basic baseline model. Then you raise complexity step by step and compare training and validation results at each step. Finally, when validation results stop improving, you stop.

1. Build a simple baseline model and note its result.
2. Increase complexity by one step and measure both errors again.
3. Then, if validation error improves, keep going.
4. If training error falls while validation error rises, step back.
5. Finally, add regularization and early stopping as a safety layer.

However, you do not always need the strongest model. A simple model is easier to explain and cheaper to maintain. Therefore, when two models give a similar validation result, we recommend choosing the simpler one.

What does overfitting look like in a sales forecast?

Imagine an e-commerce business that wants a model to forecast monthly sales. This is an example scenario, not a real client. First, the team feeds two years of history into a very complex model.

Then the model predicts past months almost perfectly. It has memorized a campaign week, a holiday effect, and even a temporary dip caused by a system outage. However, its forecast for next month misses the mark, because the events it memorized never repeated.

In the right approach, the team sets aside the most recent periods for validation. It trains the model only on earlier data and tests it on the held-out period. If the gap between training and validation is large, the team simplifies the model, keeps meaningful inputs such as seasonality, and flags one-off events so they do not teach false rules.

The key point in this example is that the evaluation imitates the future. In other words, the validation period comes after the training period. A randomly shuffled split leaks the future into the past and leads you to an optimistic measurement.

In the end, the business picks the model that predicts the future reasonably, not the one that explains the past perfectly. To check whether live experiments are meaningful, you can also use our A/B test calculator.

How do bias and variance relate to overfitting?

In machine learning, error comes from two sources: a model that is too simple, and a model that is too sensitive to the data. In statistics, the first is called bias and the second is called variance. Here we mean a purely statistical idea, not the social bias of AI systems.

For example, a very simple model has high bias. No matter how you change the data, it makes the same mistake, so it underfits. On the other hand, a very complex model has high variance. A tiny change in the training data shifts its predictions a lot, so it overfits.

There is a balance point between the extremes. In practice, as you raise complexity, bias drops while variance rises. Your job is to find the region where total error is lowest by watching validation results. This intuition sits behind every regularization and simplification decision.

However, social bias is a different topic. We cover it in our article on what is AI bias, so we will not repeat it here. Still, we only ask you not to mix the two ideas up.

How does data leakage create an illusion of success?

Data leakage means the model sees information during training that it will not have in real life. Sometimes test examples slip into the training set. Sometimes a column gives away the answer directly. Either way, the model looks spectacular, but the success is fake.

However, leakage produces the opposite picture from overfitting. Training and validation results are both wonderful, and then the model collapses in production. So before you celebrate a great result, ask one question: will we really know this information at prediction time?

• Putting the same customer's records in both the training set and the test set.
• Using a column that indirectly contains the outcome as an input.
• Predicting the past with future records in time ordered data.
• Applying cleaning steps to all the data before splitting it.

Still, the fix is simple but needs discipline. Split the data first, then learn every transformation from the training part only. Also, open the test set once, at the very end. That way your measurement stays realistic.

How does overfitting show up in marketing projects?

Prediction models are common in marketing: which lead will buy, which user will cancel, which ad audience will convert. However, all of them share the same trap. A model that fits a past campaign perfectly may not deliver the result you expect in the next one.

As an example scenario, think of a model that scores leads. A few unusual campaigns in the history become permanent rules in the model's eyes. For instance, customers who arrived only during one discount period count as the most valuable. Once the period ends, the model prioritizes the wrong people.

To prevent this, first be careful when you split campaign periods. Then train on older periods and validate on newer ones. Moreover, always compare the model with a simple rule. If the simple rule is not beaten by the model, the extra complexity brings you nothing.

You should also check the statistical significance of results on the ad side. Our A/B test calculator can help with that.

How do you monitor a model after launch?

The work does not end when a model goes live. The world changes, customer behavior changes, and the data distribution drifts. So even a model that generalized well in training can weaken over time. Still, that is not classic overfitting, but it ends in the same disappointment.

Three simple habits are enough for monitoring. First, compare the model with real outcomes at regular intervals. Second, check whether incoming data still resembles the training period. Third, set up an alert for when performance falls below a threshold.

Instead, make the retraining decision with measurement, not with a calendar. For example, if performance is stable, do not change anything. However, if it has started to drop, train on fresh data and repeat the validation and test routine from the beginning. Do not deploy a new model until you have shown on the same test setup that it beats the old one.

What is the difference between overfitting, underfitting, and a good fit?

Comparing the three situations also makes diagnosis easier. The table below summarizes training and validation behavior, typical causes, and the first response.

The table is a diagnostic frame, not a strict rule. However, in real projects the boundaries blur. Still, when you read training and validation results side by side, you usually find the right direction quickly.

Which terms get confused with overfitting?

Several concepts in the AI glossary look close to each other. Each describes a different problem, so mixing them up leads to the wrong fix. Also, the table below keeps the distinctions short. For details, follow the related articles.

To go deeper on fairness, read our article on what is AI bias. For a privacy focused way of training, read what is federated learning.

Is overfitting the same in deep learning and large language models?

The concept is the same, but the scale is different. For example, very large neural networks have enough capacity to memorize. Therefore, deep learning projects also need you to watch training and validation loss, use regularization, and split data carefully.

There is also risk when you adapt a ready made model with your own data as well. If you fine tune on a very small and narrow dataset, the model can lean too heavily on those examples. As a result, its general ability weakens, and it works well only on inputs that resemble your training sentences.

So the provider's official documentation is the authority here. Model names, sizes, and settings change quickly, so always check current recommendations in the provider's own documents. Once you know the concept, you also know which question to ask.

What is a practical overfitting checklist for businesses?

When you receive an AI or forecasting project, we suggest asking the following questions. They are practical questions you can put to your technical team or your service provider. In short, the answers give you a fast read on how reliable the model is.

• Did you report training, validation, and test results separately?
• Did you keep the test set untouched during development?
• Does the training data reflect real users and real periods?
• In time ordered data, did you make sure the future did not leak into the past?
• Did you apply safeguards such as regularization or early stopping?
• Do you monitor performance regularly after launch?
• Did you compare the model with a simple baseline?

The more "no" answers you collect, the higher the risk. Also, this is not legal or financial advice. Instead, it is only a starting frame for quality control.

What learning path should you follow after "what is overfitting"?

The best way to make the idea stick is to build a small project. First, take a small dataset, train a model, and plot the loss curves yourself. Seeing the moment when the curves separate also teaches more than a hundred sentences.

If you are starting from zero, our guide on how to get started with machine learning and our roadmap for learning AI with Python will take you forward step by step.

If you want to plan model choice, data preparation, and a monitoring routine together for a business project, our AI consulting service can help. At Talha Aslan and team, we first discuss your business goal, then your data, and only at the end the model.