If your model aces training but stumbles on real data, you may be asking what is overfitting in machine learning. It is when a model memorizes noise along with real patterns, so its predictions on new data suffer.
Overfitting is one topic inside machine learning basics, the foundations that this site explains for business readers.
| Item | Value |
|---|---|
| Common causes listed in the GeeksforGeeks guide | 5 causes |
| Recommended dataset splits | 3 sets (training, validation, testing) |
| Repeats in K-fold cross-validation | K times |
| Learning curves generated for one analysis | 2 curves |
| GeeksforGeeks guide last updated | 25 Mar 2026 |
These figures come from TechTarget and GeeksforGeeks. They can change as the guides are revised, so check the sources before you rely on them.
What is overfitting in machine learning, in plain terms?
According to TechTarget, overfitting occurs when a model excessively fits the training data, capturing both relevant patterns and inconsequential noise. The result is inaccurate predictions on new data.
A student who memorizes answers instead of understanding concepts is a helpful picture, as GeeksforGeeks describes it. That student scores well on familiar questions but fails when a new question appears, and an overfit model behaves the same way.
TechTarget offers a business example in its May 15, 2024 definition. A company screens resumes with a model that should weigh education, experience and skills, but the model fixates on font choices and rejects qualified applicants for using Helvetica rather than Times New Roman. The model has learned a quirk of the training set, not a useful rule.
Why does overfitting happen?
Most causes sit in the model, the data or the training method, according to TechTarget. The GeeksforGeeks guide, last updated 25 Mar 2026, lists five common causes:
- Model too complex: a model with too many parameters can fit noise along with real patterns.
- Training data too small: with limited data, the model may memorize examples instead of learning general rules.
- Noisy data or outliers: errors or unusual values may be learned as if they were real patterns.
- No regularization: nothing controls model complexity.
- Irrelevant features: unnecessary inputs add complexity and make overfitting more likely.
Training habits matter too. If a model trains for too long, even with optimized data and model, it starts learning noise and performs worse the longer it trains, according to TechTarget.
Another pitfall is repeatedly testing a model on the same validation data. TechTarget says this leads to implicit overfitting to a stale data set, so a model can look better than it really is.
Overfitting vs underfitting
Underfitting is the opposite problem. As TechTarget explains, the model does not fit the training data closely enough and so fails to learn the pattern in it, often because the model is too simple for a complex problem.
TechTarget’s resume example shows the contrast. An underfit model may select every resume containing keywords such as Java and JavaScript, even if only JavaScript skills are required for the position. It is too crude to separate suitable candidates from unsuitable ones.
Both failures come down to choosing the right level of complexity. If you want to see how different model families compare on that front, read machine learning vs deep learning for a side-by-side explanation.
How to detect an overfit model
The clearest sign is a model that performs well on the training data but poorly on new data, according to TechTarget. To spot that gap you need data the model has never seen, which is why the split matters. Our explainer on training data vs test data covers the difference in detail.
GeeksforGeeks recommends dividing the dataset into three sets, as its prevention guide puts it: the model learns from the training set, tuning happens on the validation set, and the final evaluation uses the test set to check real-world performance.
K-fold cross-validation
TechTarget calls K-fold cross-validation an essential tool for assessing a model, and describes the steps like this:
- Randomly split the training data into K subsets of equal size, called folds.
- Reserve one fold for validation and train the model on the remaining folds.
- Validate on the held-out fold and calculate the performance metrics.
- Repeat K times, using a different fold for validation each time.
- Average the performance metrics to get a single overall measure.
The source for these steps is the TechTarget definition. Averaging over every fold gives a steadier picture than a single split.
Learning curves
A second check uses learning curves. According to TechTarget, two curves are generated for one analysis: one on the training set to show how the model is learning, and one on the validation set to show how well it generalizes.
A learning curve for an overfitting model contains lower error values, but a gap opens between the validation and training results. When both errors plateau, adding more data does not meaningfully change the fit.
How to reduce overfitting
TechTarget says organizations must improve both the model and the data. The GeeksforGeeks guide adds practical options, which you can combine:
- Simpler models: fewer parameters reduce the chance of fitting noise.
- Feature selection: remove irrelevant or redundant features so the model focuses on meaningful information.
- Regularization: add a penalty to the loss function so the model cannot become too complex.
- Cross-validation: test on multiple splits for a more reliable performance estimate.
- Proper data splitting: keep training, validation and test data separate.
- Ensemble methods: techniques like Random Forest combine multiple models to reduce variance.
Start simple and select features
TechTarget advises beginners to start with the simplest algorithm to avoid complications and improve generalization, and names k-means clustering and k-nearest neighbors as simple algorithms that are easier to interpret and debug. Feature selection then identifies the most useful inputs, which reduces the model’s dimensionality, per the TechTarget definition.
Regularization in practice
Regularization puts constraints on a model during training. In TechTarget’s account it adds a term to the loss function, the measure of the difference between predicted outputs and actual target values, and then solves for the weights that minimize the result.
Two common forms differ in how they treat the weights:
- Ridge regression adds the sum of the squares of the weights to the loss function, keeping coefficients as small as possible without reducing them to zero.
- LASSO regression adds the sum of the absolute values of the weights to the loss function.
GeeksforGeeks describes the same idea as L1 and L2 regularization: L1 can remove less important features, while L2 reduces their influence.
Frequently asked questions
Is overfitting the same as underfitting?
Show answer
No. Overfitting means the model fits the training data too closely, noise included, while underfitting means it does not fit closely enough to learn the pattern, as TechTarget explains.
How can I tell whether my model is overfitting?
Show answer
Look for strong results on training data and weak results on new data. Cross-validation and learning curves make that gap easier to see, according to TechTarget.
Will more data fix overfitting?
Show answer
It can help when a small training set is the cause, since GeeksforGeeks lists limited data as a reason models memorize examples. A learning curve shows whether more data is worthwhile: once training and validation errors plateau, extra data does not meaningfully change the fit.
Why is testing on the same validation data repeatedly a problem?
Show answer
TechTarget warns that it leads to implicit overfitting to a stale data set. Keeping a separate test set for the final check avoids that, as the GeeksforGeeks guide recommends.
