A model reaches a 98% score in testing, causing the team to think the work is finished. After deployment, the model's predictions turn out to be inaccurate. The team then watches a dashboard that displays unexpected results. The forecast was accurate when used on last year's data, yet it does not deliver correct results for the current quarter.
This typically occurs due to a single cause known as overfitting.
Overfitting happens when a machine learning model learns the training data excessively, memorising particular examples and random variations rather than the overall trend. The model works extremely well on the training data yet performs poorly on new data. Remedies involve using more or higher-quality data, selecting simpler models, applying regularisation techniques, and employing validation splits to detect the issue before deployment.
Overfitting takes place when a machine learning model learns the specific details of its training data to an extreme degree. Rather than discovering the underlying patterns, the model memorises the individual examples and the noise present in the dataset.
The model performs well on its training data, yet it fails when it meets new information.
People frequently liken this to a student who memorises a previous exam paper. That student gets a perfect score on the practice test but fails when the actual questions differ.
Shipshape Data builds data and AI systems. We often see this particular failure when projects stall between the initial notebook phase and a reliable business application. This guide describes what overfitting is, how to spot it early, what causes it, and which techniques effectively stop it. It also addresses the related problem of underfitting. The aim is to give practical steps you can apply to a model this week instead of focusing on theory.
Why overfitting matters
The purpose of training a model is generalisation. This process lets a model apply what it learned from existing data to new data. Overfitting eliminates this capability. While the model keeps producing results and seems confident, it relies on memory rather than understanding. This reliance on memory renders the model ineffective when the input changes.
The performance gap between training and production is significant. Accuracy is high during development, yet the model makes incorrect predictions with high confidence in a live environment. This occurs because the model identified patterns in historical data that do not reflect the true drivers of the outcome. For example, a churn model that memorised the identities of customers who left last year does not give useful information about which customers will leave next year.
An overfit model repeats specific data rather than learning general patterns. This makes the model fail when it encounters new or different information.
The business cost
Technical failures lead to commercial failures. Organisations make decisions based on inaccurate predictions. They spend marketing budget on the wrong audiences, generate incorrect stock forecasts, and give customers poor product recommendations. These errors diminish the trust that the organisation has in the AI programme. When stakeholders no longer trust the outputs, they typically stop funding the project.
We have seen teams repeatedly retrain and test their models without success. These efforts fail because the issue is not the volume of training data. Instead, the problem lies in the quality of the training data and the lack of measurement of the difference between the development environment and the live system. Organisations often fail to identify this gap until they have already incurred financial losses.
Overfitting is easy to identify but costly to overlook. It is problematic to wait until after a product has launched to look for this issue.
How to spot it early
It is less expensive to correct overfitting during the training process than after a model has been deployed. You should monitor the model continuously while it trains to spot this issue early. The most reliable sign of overfitting is a growing gap between the model's performance on training data and its performance on validation data.
Monitor both metrics during the training process. If training accuracy reaches 98% while test accuracy stays at 65% or starts to drop, the model is overfitting. In a successful training session, both metrics improve at a similar rate. A widening gap between these numbers shows that the model is no longer identifying general patterns and is instead memorising the specific training data.
The learning curve is a visual representation of this concept. You can plot performance against the amount of training data or the number of training rounds for both the training and validation sets. The resulting shape explains the model's behaviour. If the two curves rise and stay close to each other, the model is learning effectively. If the training curve improves quickly while the validation curve remains low or begins to decline, the graph shows overfitting. These plots are easy to generate and reduce uncertainty about how the model is performing.
Cross-validation
A single split of data into training and testing sets can produce misleading results, as that specific split may be unrepresentative. K-fold cross-validation provides a more reliable assessment. You divide the dataset into several subsets, train the model on some parts while testing on the remainder, and repeat this process for every combination before averaging the results. Consistent scores across all folds indicate that the model generalises well. If the scores vary significantly between folds, the model's performance depends on the specific examples it encountered, which indicates a lack of stability.
What causes overfitting
Most cases stem from three main factors: a model that is too complex for the data, an insufficient amount of data, and features that contain more noise than signal. These issues usually appear together. A complex model might work correctly with more data, while a simple model can fail if it uses noisy features. Both situations lead to the same outcome.
Too much model for the data
Overfitting occurs when a model has more capacity and parameters than the training data requires. A deep neural network with millions of weights can memorise a dataset instead of learning patterns. A decision tree without a depth limit will keep splitting until each training example is in its own branch. This causes the model to treat random variations as meaningful signals. Each extra parameter lets the model record noise that it should ignore.
Not enough data
Small datasets force a model to draw conclusions from limited evidence. If you train a model on 100 rows with 50 features, it will identify relationships that are coincidental. These patterns exist in your specific sample but not elsewhere. The model cannot separate a real effect from two variables that happen to align in a small number of records. Increasing the amount and variety of data is usually the most effective solution. Genuine patterns repeat across large samples, while coincidences disappear as the dataset grows.
Noisy or redundant features
Including irrelevant or highly correlated features gives a model more chances to produce inaccurate results. Redundant inputs let the model find multiple ways to reach a memorised answer. If a feature correlates with the target by chance in the training set, the model will treat it as a valid signal. Precise feature selection and engineering removes these noisy inputs and prevents the model from relying on false patterns.
Techniques that actually prevent it
No single technique can completely prevent overfitting. Good outcomes are achieved by mixing model constraints, larger datasets, and honest validation. The techniques listed below are the most common tools for this goal, together with the particular benefits each offers.
Early stopping
If models are trained for too long, they will eventually start to memorise the data. Early stopping avoids this by terminating the training at the optimal moment. During training you watch the validation loss and halt the process when that loss begins to rise consistently, even though the training loss keeps decreasing. A rising validation loss signals that the model is beginning to fit noise in the data. Applying this method conserves computing resources without lowering performance, but you must differentiate a genuine upward trend from normal curve fluctuations.
Regularisation
Regularisation introduces a penalty to the loss function that depends on model complexity. This encourages the model to discover simpler patterns that generalise more effectively to new data. Two common types exist. L1 regularisation, also known as Lasso, drives some weights to zero. This serves as feature selection by eliminating inputs that do not significantly contribute to the model. L2 regularisation, or Ridge, shrinks large weights to stop any single feature from exerting excessive influence on the prediction. Both methods favour a smooth, general fit rather than one that matches the training data exactly. More details on related approaches are available in our notes on AI methods and techniques.
Data augmentation
If you cannot acquire more real data, you can sometimes generate useful variation from the data you already possess. Data augmentation produces modified copies of existing examples. For images, this may involve rotating, flipping, or adjusting brightness. For text, it can involve substituting synonyms or rephrasing sentences. Each variant still reflects the same underlying case, allowing the model to learn important features rather than memorising specific pixels or wording. This makes memorisation harder and generalisation easier, which is the intended outcome.
Honest validation splits
Split the data three ways, not two: a training set to fit the model, a validation set to tune hyperparameters and watch for overfitting, and a test set touched exactly once, at the very end, for a final honest read. The discipline that matters here is keeping that test set sealed. Every time you look at it and adjust something in response, you leak a little information back into the model and overfit to the test set itself, which leaves you with no clean measure of anything at all.
Ensembles
Ensemble methods merge multiple models to lessen the impact of individual errors. A random forest trains several decision trees on different data subsets and averages their results. Boosting trains models in sequence. Each new model tackles the errors of the preceding one; different models overfit in various ways. Merging them eliminates specific memorisation and reveals underlying patterns. This approach raises model accuracy without needing manual adjustments to each parameter.
No single one of these methods solves the problem on its own. Applying all of them at once is an error, since heavy regularisation on a simple model can cause underfitting. You need to match the specific solution to the cause of the issue. A model lacking sufficient data needs more data. A model that overfits needs a penalty term. A team that examines its results too early needs a sealed test set.
Overfitting and underfitting
Underfitting is the opposite of overfitting, and avoiding one can directly cause the other. Underfitting happens when a model is too simple to capture the underlying patterns in the data. For example, using a straight line to model data that follows a curve will cause the model to miss the trend. This leads to poor predictions on both training and test data because the model lacks the capacity to learn the pattern. This condition is marked by high bias and low variance, meaning the model is consistently wrong for the same reason.
Overfitting happens when a model shows a low error rate on training data but a high error rate on new data. Underfitting means the model learned too little, whereas overfitting means it learned specific noise instead of general patterns. You should aim for a balance where the model captures the actual structure of the data. You can locate this point by checking whether the accuracy is similar for both the training and test sets. A large gap between these two values signals overfitting. If both values are low, the model is underfit.
A practical approach is to start with simple models and add complexity step by step. Begin with a baseline, for example a linear model or a shallow tree. Use more complex models only when the validation data supports the change. Keep track of training and validation metrics at each stage of the process. Cease adding complexity once test performance stops improving, even if the training score continues to rise. The objective does not involve sophistication. The objective is to guarantee that the model works reliably on data it has not seen before.
How to apply these ideas
Knowing theoretical concepts differs from using them in a project. Actual work often includes messy data and tight deadlines. When a business demands quick results, you might be tempted to skip the validation process. Yet, skipping this step is especially dangerous in such situations. Prevent overfitting by keeping consistent practices instead of depending on one complex technique.
Start with the data
Overfitting typically occurs due to the state of the data prior to modelling. First clean the data by eliminating duplicates, applying a uniform approach to missing values, and handling outliers that could distort the model. Divide your data before starting exploration and avoid accessing the test set thereafter. Document each preprocessing action so you can reuse the same treatment on future data. The unstructured data we handle for clients frequently includes noise that leads models to memorise patterns instead of learning them, making this step essential.
Build up, not down
Employ the simplest architecture that is expected to work and increase complexity only when validation data supports it. Set up a baseline with a linear model or a shallow tree before looking at more advanced alternatives. Introduce complexity incrementally while watching training and validation metrics to see when extra capacity stops enhancing performance. This method lowers computational expense and avoids deploying models that are more fragile than necessary for the task.
Watch it after launch
A model that starts out performing well may lose accuracy over time as the training data no longer reflects the present environment. Deploy models with monitoring tools that measure accuracy and trigger alerts when predictions drop beneath a defined threshold. In such cases, retrain the model with fresh data that mirrors current conditions. Regularly assess if your features remain pertinent and whether the model architecture still suits the problem. Overfitting can occur beyond the training phase. A stale production model exemplifies the same kind of failure.
Where to start
The most important point in this discussion is the gap.
The disparity between a model's performance on training data and its performance on new data serves as the most reliable metric in the process. Split your data properly and apply cross-validation. By monitoring the gap between these two metrics, you will catch most overfitting problems before deploying the model to production.
Creating models that work effectively with real-world data demands careful data preparation, a practical architecture, and continuous monitoring after launch. All of these elements rely on a data foundation you can trust. That is the service we deliver. If your organisation is moving AI solutions from development to production, talk to us and we will assist in spotting potential problems before they become issues.
Wondering what this means for your own organisation? Our AI strategy service assists you in identifying the first profitable use case and the sequence for building the remaining ones.
Frequently asked questions
What is overfitting?
Overfitting takes place when a machine learning model captures the specific details and noise of its training data rather than the underlying pattern. The model shows high accuracy on the original data but does not produce correct results on new information. This is akin to a student who memorises answers from a previous exam and cannot answer different questions on a new test.
What is the difference between overfitting and underfitting?
Overfitting happens when a model shows a low error rate on training data but a high error rate on new data. This is caused by the model memorising specific details rather than learning the true pattern. Underfitting is the opposite problem, where a model is too simple to capture the underlying data. Consequently, it performs poorly on both training and test data. The ideal balance between these states is identified when the model displays similar accuracy on both datasets.
How do you prevent overfitting?
Overfitting cannot be addressed by a single method. The most effective strategy combines early stopping, regularisation, data augmentation, validation splits, and ensembles. Early stopping halts training when validation loss starts to increase while training loss keeps decreasing. Regularisation imposes a penalty for complexity, encouraging the model to find a simpler, more general fit. Keeping a separate test set that is accessed only once at the end of the process is as essential as any modelling technique.
How do you prevent overfitting in linear regression?
These techniques are also applicable to linear regression. Regularisation methods such as L1 (Lasso) and L2 (Ridge) penalise model complexity by removing weak features or reducing large weights, preventing any single input from dominating. You need to use clean, sufficient training data and keep a distinct validation split, as these are required for any model. Start with the simplest model version and add complexity only when validation results show it is needed.
How does data augmentation reduce overfitting?
Data augmentation generates new training examples from existing data. For images, this involves rotating, flipping, or adjusting brightness. For text, it consists of replacing words with synonyms or rephrasing sentences. Since each version reflects the same original case, the model must identify general features instead of memorising specific pixels or words. This process stops the model from memorising the data and helps it generalise to new information, thereby reducing overfitting.