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A model may be overfitting when it scores much better on its training data than on data held out from fitting. That gap is a warning, not a verdict: first make sure the held-out data reflect the examples the model will face in use, and that preprocessing has not leaked information across the split.
How do I know if my model is overfitting?
Compare its training score with its validation score using the same scoring metric and a split that represents the prediction task. A consistently high training score paired with a materially lower validation score is a classic overfitting pattern. Scikit-learn’s validation-curve guide contrasts it with underfitting: low scores on both training and validation data point toward a model that is not capturing the task well.
A strong training result by itself says only that the model fits examples it has already seen. As the scikit-learn developers explain in Cross-validation: evaluating estimator performance, “Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would have a perfect score but would fail to predict anything useful on yet-unseen data.”
Read the score pattern, not just one number
- High training, lower validation: likely overfitting, or a problem with how the evaluation data were chosen or processed.
- Low training, low validation: likely underfitting; the model may be too constrained, the features may be uninformative, or the representation may not suit the task.
- Similar, strong scores: encouraging evidence of generalization under this evaluation setup, but not proof of future performance if deployment data differ.
Scikit-learn’s validation-curve guide puts the first pattern plainly: “If the training score is high and the validation score is low, the estimator is overfitting and otherwise it is working very well.” Treat that as a diagnostic starting point rather than proof that model complexity is the only cause.
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Why is my training score higher than my test score?
Some gap is expected because a fitted model has had the opportunity to adapt to its training examples but not to held-out ones. A large or persistent gap can reflect overfitting, but it can also signal that the evaluation does not match the intended use or that information crossed the boundary between training and evaluation.
Check for leakage in preprocessing
If a transformation learns from data—such as scaling or feature selection—fit it only on the relevant training portion. Fitting it once on the full dataset before splitting allows information from evaluation examples to influence the transformation. In scikit-learn, put the transformations and estimator in a Pipeline; when the pipeline is passed to cross-validation or parameter search, each fold fits the transformations on that fold’s training subset. See the official common pitfalls guide.
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Check whether the split matches the prediction task
Decide what “unseen” means in practice. If examples are independent, a suitable held-out split or cross-validation may fit. If records belong to people, devices, sites, or other groups, keep related records together when deployment requires prediction for unseen groups. If the task predicts future outcomes from ordered or time-dependent data, an arbitrary random split may allow the evaluation to differ from the real forecasting problem. Scikit-learn documents group-aware and other cross-validation splitters and notes that ordering can affect whether shuffling is appropriate.
Choose a metric that reflects the decision
A score is informative only in relation to what the model must do. Select an appropriate metric for the task rather than assuming a default score answers the practical question. Scikit-learn’s model-evaluation documentation describes scoring choices available across its evaluation tools.
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How to check for overfitting with cross-validation
- Define the unseen-data scenario. Decide whether evaluation examples are independent, grouped, or ordered, and select a split strategy that mirrors the intended prediction setting.
- Choose the scoring metric. Use a metric tied to the task and its consequences; keep that metric consistent when comparing training and validation performance.
- Separate data before learning transformations. Use a scikit-learn
Pipelinecontaining preprocessing and the estimator so each cross-validation training fold learns its own transformations. - Evaluate across folds. Compare training and validation scores across folds, including their variability, rather than relying on a single result. A persistent gap is more concerning than a difference driven by one unusual fold.
- Keep a final test set untouched during model choices. Use it for evaluation after tuning is complete. Repeatedly checking its score while selecting hyperparameters lets information from that test set influence the selection.
Cross-validation is useful for comparing candidate settings, but it does not make every split valid: the folds still need to match the data structure and intended use. Also, do not interpret a validation score used repeatedly for tuning as an untouched final estimate.
When to use nested cross-validation
If you need to estimate the performance of the full process that includes hyperparameter tuning and model selection, nested cross-validation separates those jobs: inner folds select settings, while outer folds evaluate the selection procedure. Scikit-learn discusses this distinction in its nested versus non-nested cross-validation example.
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How do I plot a learning curve in scikit-learn?
Use learning_curve to see how training and validation scores change as the amount of training data changes. Use validation_curve to see how those scores change across values of one hyperparameter. Both tools accept an estimator and a cross-validation strategy; for leakage-safe preprocessing, pass a pipeline rather than preprocessing the complete dataset first. The official API references are learning_curve and validation_curve.
Use a validation curve to inspect complexity or regularization
Choose a consequential hyperparameter, such as one that controls model complexity or regularization, and plot training and validation scores across its values. If training performance continues to rise while validation performance peaks and then falls, that pattern suggests a generalization tradeoff. Confirm it with an appropriate split strategy; do not repeatedly consult the final test set to choose the hyperparameter.
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Use a learning curve to assess whether more data may help
Plot scores against training-set size when you want to know whether adding examples may reduce a gap. If training and validation scores remain separated as the training set grows, the curve can inform whether more data are likely to help, but it is not a guarantee. The result depends on the task, the chosen metric, and the evaluation design.
What should I do if the model appears to be overfitting?
Start by validating the diagnosis: audit preprocessing for leakage, verify that groups or time order are handled appropriately, and check whether the score gap persists across folds. If it does, use the validation curve to assess less complex or more regularized settings. If the learning curve indicates that validation performance improves as training size grows, acquiring more representative data may help. Re-evaluate any revised model using the same task-appropriate procedure, reserving the final test set for the end.
Scikit-learn documentation identified version 1.9.1 as the current stable documentation version when this guidance was prepared; API details may change. The score patterns are diagnostic guidance, not a claim that any particular model has been tested or that one remedy will work for every dataset.
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