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Compare the model on the data used for training with representative data it did not use for fitting. Poor results on both sets usually indicate underfitting. Strong training results followed by substantially weaker held-out results indicate overfitting. The comparison is meaningful only when you use a task-appropriate metric, prevent leakage, and make the held-out data resemble the cases the model will encounter in use.
These are diagnostic patterns rather than automatic proof. A misleading split, unsuitable metric, label problems, or a shift between training and deployment data can mimic either condition.
Start with the training-versus-held-out comparison
Use the same evaluation measure on the training set and on data that was kept out of fitting. The measure should reflect the prediction task and the cost of mistakes: accuracy can be useful for some classifiers, while other tasks require a different score. There is no universal threshold that defines an overfitting gap for every model or dataset.
| Training performance | Held-out performance | Most likely interpretation |
|---|---|---|
| Poor | Poor | Underfitting is likely. The model may lack capacity, the features may not express the relevant relationship, regularization may be too strong, or training may be insufficient. |
| Strong | Substantially weaker | Overfitting is likely. The model may have learned details, noise, or artifacts specific to the training sample. |
| Strong | Strong and reasonably close | The fit may be appropriate, provided the held-out examples represent intended use and the metric measures the real objective. |
Google describes an overfit model as one that “makes excellent predictions on the training set but poor predictions on new data” in its Overfitting guidance. AWS presents the same training/evaluation patterns in its Model Fit: Underfitting vs. Overfitting documentation.
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Make the comparison trustworthy before diagnosing fit
Use one metric that matches the task
Choose a score that reflects what “good” means for the application, then calculate it consistently on training and held-out data. A metric can hide important failures if it ignores class imbalance, the relative cost of false positives and false negatives, ranking quality, calibration, or the size of numerical errors. Compare like with like: changing the metric between datasets can create an apparent gap that is only a measurement artifact.
Keep evaluation data out of fitting and feature decisions
Held-out examples must not influence parameter fitting, feature engineering, preprocessing choices, or repeated model selection. Fit transformations such as scaling or imputation on the training portion and apply the learned transformation to held-out data. Look for duplicated records, shared users, or other paths by which information from the evaluation portion could leak into training.
Check that the split represents deployment
Generalization estimates are useful when evaluation examples resemble the cases the model will face. Google identifies independent examples, a sufficiently stationary dataset, and similar distributions across partitions as conditions that support generalization; see its data and generalization guidance. A random split can be misleading when records from the same person, device, customer, site, or event appear in both partitions, or when the future differs systematically from the past.
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A step-by-step diagnostic workflow
1. Define the evaluation question
- State what the model will predict and which errors matter operationally.
- Select the metric or set of metrics that represents that objective.
- Decide what constitutes a representative held-out example, including time period, geography, group, or operating condition.
This prevents you from labeling a model “good” because it optimizes a convenient score that does not match its real use.
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2. Establish a clean baseline split
- Separate training data from validation data used during development.
- Keep a final test set untouched until model selection is complete.
- Apply the same split logic to every candidate model so their scores are comparable.
- Audit for duplicates, leakage, label errors, and distribution differences before reading the scores.
If observations are related by time, group, or another dependency, use a split that preserves that structure rather than assuming random sampling is valid.
3. Measure both sides with the same protocol
- Fit the model only on the training partition.
- Compute the selected metric on that training partition.
- Compute the identical metric on the held-out partition.
- Record the absolute scores and their difference; also inspect class- or segment-level results where a single aggregate could conceal failures.
Poor results on both partitions point toward underfitting, but investigate data quality and the evaluation design before changing model complexity. A strong training score paired with a much weaker held-out score points toward overfitting, provided the held-out data are credible.
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- Verify that labels are defined and recorded consistently.
- Check whether the training set contains information unavailable at prediction time.
- Compare feature distributions and target rates between partitions.
- Identify sparse subgroups or operating conditions that are overrepresented in one partition.
- Confirm that preprocessing was learned only from the training data.
A model can appear to overfit when the held-out set comes from a different population, and it can appear to generalize when duplicates or leakage make the task artificially easy.
Use training and validation curves to see when the problem develops
Plot loss or another suitable score against training steps or epochs for both training and validation data. A single loss curve shows behavior on one dataset; placing the training and validation curves together exposes the generalization gap over time.
Pattern that suggests overfitting
Training loss continues to fall while validation loss begins to rise. Google’s deep-learning tuning guidance calls this “problematic overfitting” and states that it occurs when validation error starts increasing during training; see A scientific approach to improving model performance. The model is improving on examples it has seen while becoming worse on examples it has not seen.
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Pattern that suggests underfitting
Training performance remains weak, often with validation performance similarly weak. Both curves may level off at an unsatisfactory value. This can mean the model cannot represent the relationship, useful signal is absent from the features, regularization is suppressing too much flexibility, or optimization has not progressed far enough.
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Pattern consistent with useful generalization
Training and validation curves improve in a broadly similar way and finish at reasonably close values. Similar shapes are a better sign than a widening separation, but they do not replace a final evaluation on credible, untouched test data.
Do not treat the first small rise or dip as conclusive. Check whether the change persists across checkpoints, random seeds, or folds and whether the validation set is large and representative enough for the metric to be stable.
Validation curves and learning curves answer different questions
Validation curves: “Which setting is too simple or too flexible?”
A validation curve varies one hyperparameter—such as a complexity, depth, or regularization setting—and plots training and validation scores for each value. It can reveal settings that underfit because both scores are low, and settings that overfit because training score stays high while validation score drops. The scikit-learn reference describes this method at scikit-learn validation curves.
Change one factor at a time when possible, document every choice, and avoid using the same validation score as your final reported performance after extensive tuning.
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Learning curves: “Would more training examples help?”
A learning curve varies the amount of training data and plots training and validation performance against that amount. It helps distinguish a data-limited situation from one in which additional examples are unlikely to solve the problem.
- A large training–validation gap that narrows as the training set grows is consistent with high variance; more representative data may help.
- Both curves remaining low and close suggests high bias or underfitting; simply adding data may not fix a model that cannot approximate the underlying relationship.
- Curves that have already plateaued suggest that changing features, capacity, objective, or data quality may matter more than collecting more examples.
More data is not automatically useful. Scikit-learn cautions that additional examples help when the underlying function is too complex to be approximated by a lower-variance estimator; otherwise, a different intervention is needed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use cross-validation when one split is too fragile
In k-fold cross-validation, divide the development data into k folds, train on k−1 folds, evaluate on the remaining fold, and repeat until each fold has served as the holdout. Aggregate the fold scores to see how sensitive the result is to the particular split. AWS explains this procedure in Cross-Validation.
Cross-validation can make the diagnosis more reliable when data are limited, but it does not excuse leakage or a wrong split strategy. Use grouped folds when records from the same entity must stay together, and time-aware folds when future observations must not influence past training. A random fold assignment can produce an overly optimistic score when related observations cross the boundary.
After selecting the approach, evaluate once on the untouched final test set. Report the test metric with the split definition and the conditions under which it was measured; do not present a cross-validation score repeatedly used for tuning as an independent final estimate.
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Match the remedy to the observed pattern
| Observed evidence | Investigations and possible changes |
|---|---|
| Poor training and held-out performance | Check feature usefulness and data quality; determine whether the model has enough capacity; test whether regularization is excessive; verify that training has run sufficiently. AWS lists adding domain-specific features and reducing regularization as ways to increase flexibility. |
| Strong training performance and much worse held-out performance | Check representativeness, leakage, duplicates, and distribution shift. Then consider reducing flexibility, selecting fewer features, or increasing regularization. AWS lists feature selection and stronger regularization as possible responses. |
| Validation loss rises while training loss falls | Try an earlier stopping point or a regularization change. Select the checkpoint using validation data, then assess that selection on data not used for the decision. Google recommends additional regularization techniques when problematic overfitting appears. |
| Both scores are weak and a larger dataset is being considered | Inspect learning curves first. If both curves are low and close, improve capacity, features, objective, or training rather than assuming more examples will help. If a large gap narrows with more data, collecting representative examples may be justified. |
Each intervention should be treated as a new experiment: change the part of the pipeline implicated by the evidence, rerun the same evaluation protocol, and record whether the held-out result improved without creating a new gap.
Common reasons the diagnosis goes wrong
A convenient random split hides dependence
Rows from one customer, patient, device, or location can be nearly duplicates. If some appear in both training and validation, the held-out score may look excellent even though the model will fail on a genuinely new entity. Grouped or time-ordered splitting is safer when those dependencies exist.
The validation set has become a training signal
Every manual choice based on a validation score—feature removal, architecture selection, preprocessing, or stopping epoch—uses information from that set. After many such choices, the score can be optimistic. Keep a final test set untouched, and limit test-set inspection to the final assessment.
The metric rewards the wrong behavior
An aggregate score can conceal poor performance for a costly class or subgroup. Review the metric definition and inspect relevant slices before concluding that a model is underfitting or overfitting.
Deployment data have shifted
A model may fit its training distribution and still perform poorly after a change in users, sensors, policy, geography, season, or time period. That is a generalization and data-distribution problem, not necessarily evidence that the model is intrinsically too complex. Build evaluation partitions that reflect the change you need to withstand.
Small score differences are overinterpreted
Validation estimates vary with the sample. Use repeated or cross-validated measurements when appropriate, inspect uncertainty and subgroup behavior, and look for a persistent pattern rather than reacting to one marginal difference.
A compact checklist for a fit diagnosis
- Is the metric appropriate for the task and applied identically to training and held-out data?
- Was the held-out partition excluded from fitting, preprocessing, feature selection, and hyperparameter choices?
- Does the split represent the time, groups, geography, and operating conditions of intended use?
- Are labels, duplicates, leakage paths, and distribution differences understood?
- Are training and held-out scores both recorded, along with their gap?
- Do training and validation curves show a widening gap or a validation loss that rises while training loss falls?
- Would a validation curve clarify the effect of a hyperparameter, or would a learning curve clarify the value of more data?
- Is cross-validation designed around the dependencies in the observations?
- After any change, will the model be checked again on data not used to make that change?
Underfitting is primarily a failure to capture enough of the useful relationship; overfitting is primarily a failure to carry what was learned into new, representative cases. The training-versus-held-out comparison, supported by curves and a sound split, tells you which problem you are actually trying to fix.
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