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Training data teaches a machine-learning model; testing data checks how the selected model performs on examples kept out of development. To make that check meaningful, reserve the test set before fitting preprocessing or choosing models, use training data and validation or cross-validation for development, and evaluate the test set only after those choices are settled.
Training data vs. testing data
| Data split | What it is used for | What must stay out of it |
|---|---|---|
| Training | Fit model parameters using its features and, in supervised learning, labels. Fit data-dependent transformations such as scaling and imputation here too. | Nothing that belongs exclusively to the final evaluation; training examples are expected to influence the fitted model. |
| Validation | Compare candidate models and tune settings during development. Cross-validation rotates which development folds serve as validation and combines their scores. | The final test set. Validation results may guide development, so they are not an independent final estimate. |
| Testing | Estimate performance of the selected modeling process on held-out examples. | Model fitting, feature selection, preprocessing fit, and decisions about which model or settings to choose. |
A model may score very well on data it has already seen and still fail on unseen cases. As the scikit-learn cross-validation guide explains, testing a prediction function on the same data used to learn its parameters is a methodological mistake: even a model that merely repeats seen labels could score perfectly without being useful on new examples.
What a test score can—and cannot—tell you
A test score estimates how the selected process performs under the conditions represented by the held-out split and the chosen metric. It is not a guarantee of future real-world performance. A test set that differs from deployment data, or that shares hidden dependencies with training data, may give a misleading picture of how the model will behave later.
The scikit-learn guide’s Iris/SVM demonstration uses 150 examples, assigning 90 to training and 60 to testing, and reports a classifier score of 0.96. That is an illustration using that sample and setup, not a recommended universal split ratio or a general accuracy benchmark.
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Why you need validation as well as a test set
Use validation data or cross-validation to make development choices: compare algorithms, select features, and tune hyperparameters. Cross-validation can use a small dataset efficiently by rotating the validation fold, though it requires repeated fitting and can cost more computation.
Keep the test set outside those choices. If you repeatedly change the model in response to its test score, that score has become feedback in development. The result is no longer an independent final check and can be optimistically biased. The scikit-learn pitfalls guide puts it plainly: “Test data should never be used to make choices about the model.”
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How to prevent preprocessing leakage
Data leakage occurs when model-building uses information that would not be available at prediction time, as described in the scikit-learn guide to common pitfalls and recommended practices. Leakage is not limited to using test labels. If you calculate scaling values, impute missing values, select features, or reduce dimensions using the complete dataset before splitting, information from the held-out observations has influenced the pipeline.
- Split first. Set aside the final test portion before fitting transformations, selecting features, or comparing models.
- Fit on training data. Learn each data-dependent transformation and the model parameters using only the relevant training portion.
- Apply, do not refit, on held-out data. Use the learned transformation to process validation and test examples. In scikit-learn terms, call
fitorfit_transformon the training portion andtransformon held-out portions. - Keep cross-validation fold-safe. Put preprocessing and the estimator in a scikit-learn
Pipelineso that each transformation is fitted within the training fold, rather than once on all folds. - Evaluate once the process is selected. Use the reserved test set for the final evaluation, not as a recurring tuning dashboard.
Choose a split that matches the prediction task
A random split is suitable only when randomly allocated examples are independent in the way the intended use requires. Before splitting, look for repeated people, devices, accounts, or other shared sources, and consider whether prediction is meant to be about future observations. The appropriate boundary depends on what the model will face after deployment.
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| Split strategy | Use it when | Important limitation |
|---|---|---|
| Random | Examples can be randomly allocated without breaking the independence assumptions relevant to the prediction task. | May put related records on both sides of the split. |
| Stratified | Class proportions should remain approximately similar across folds, particularly when a class is rare and a fold might otherwise omit it. | Does not prevent records from the same entity or time period crossing the boundary. Stratification can also make folds more homogeneous and observed score variation artificially narrower. |
| Group-aware | Multiple records belong to the same person, device, account, or other entity, and the evaluation should test on entities not used for fitting. | May leave fewer usable examples per split, especially when there are few groups. |
| Time-aware | The model will predict future observations from past data, such as in forecasting. | Randomly mixing dates can allow future information to inform training and produce an unrealistic evaluation. |
Stratification addresses class balance, not dependence. The scikit-learn model-selection API lists group-aware and time-series splitters; its train_test_split helper does not account for groups.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How much data should go to testing?
There is no single test percentage that fits every dataset. The scikit-learn API lets you specify a proportion or an absolute count; the guide’s 40% Iris example is illustrative, not a default recommendation. Choose a split that leaves enough data to fit the model while retaining enough independent examples for a useful evaluation. Consider class frequencies, group or time dependencies, computational cost, and how much the metric varies across validation folds.
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Document the split design and the prediction setting it is intended to represent. A score is easier to interpret when readers know what was held out and whether the split reflects the cases the model is expected to encounter.
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A safe workflow at a glance
- Define the future prediction setting and identify entity or time dependencies.
- Choose a random, stratified, group-aware, or time-aware split that reflects that setting.
- Set the final test data aside before learning any preprocessing or selecting features.
- Fit transformations and candidate models on training data; use validation data or cross-validation to choose among them.
- Apply the selected pipeline to the untouched test set and report its score as an estimate under that split, not a promise about every future population.
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